scikit-misc
Miscellaneous tools for scientific computing.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10; compiled wheels are provided for common platforms, but source builds require a C and Fortran compiler.
- 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 · maintenance · safety
permissive license (permissive) — 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.
last release 2025-11-03 (284 days) · last repo commit 2025-11-03 · 47 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 167,555 downloads/mo, #10,467 on PyPI
Alternatives
Verify before relying
pip install scikit-misc
import numpy as np
# Use scikit-misc functions alongside numpy for scientific tasks- 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?
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 on this page. Verify before relying on it.
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
scikit-misc on PyPI
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.
Requires Python >=3.10; compiled wheels are provided for common platforms, but source builds require a C and Fortran compiler.
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 tasksVerify 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 | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagenumpy |
| Maintenance | Aging 284 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 167,555 / month, #10,467 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: CProgramming Language :: FortranProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering |
Evidence: scikit_misc-0.5.2-cp310-cp310-macosx_10_9_x86_64.whl; scikit_misc-0.5.2-cp310-cp310-macosx_11_0_arm64.whl; scikit_misc-0.5.2-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; scikit_misc-0.5.2-cp310-cp310-win_amd64.whl; scikit_misc-0.5.2-cp311-cp311-macosx_10_9_x86_64.whl; scikit_misc-0.5.2-cp311-cp311-macosx_11_0_arm64.whl; scikit_misc-0.5.2-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; scikit_misc-0.5.2-cp311-cp311-win_amd64.whl; scikit_misc-0.5.2-cp312-cp312-macosx_10_13_x86_64.whl; scikit_misc-0.5.2-cp312-cp312-macosx_11_0_arm64.whl; scikit_misc-0.5.2-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; scikit_misc-0.5.2-cp312-cp312-win_amd64.whl; scikit_misc-0.5.2-cp313-cp313-macosx_10_13_x86_64.whl; scikit_misc-0.5.2-cp313-cp313-macosx_11_0_arm64.whl; scikit_misc-0.5.2-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; scikit_misc-0.5.2-cp313-cp313-win_amd64.whl; scikit_misc-0.5.2-cp314-cp314-macosx_10_15_x86_64.whl; scikit_misc-0.5.2-cp314-cp314-macosx_11_0_arm64.whl; scikit_misc-0.5.2-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; scikit_misc-0.5.2-cp314-cp314-win_amd64.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “scientific computing utilities”
- scikit-miscscikit-misc provides miscellaneous tools for data analysis and…
- audmathaudmath provides mathematical functions implemented in pure Python…
- lscsoft-glueLSCSoft-GLUE provides utilities and grid access tools for running…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also scikit-build · simsimd · traittypes · colorcet · fckitlib · rasterstats · gensim · scverse-misc · xarray