scikit-misc

library0.5.2pypypi✓ verified 85d ago

scikit-misc is a Python library providing a collection of miscellaneous tools for data analysis and scientific computing. It includes algorithms that were once part of SciPy's `scipy.misc` module but were removed or deemed unstable, now offered in a more robust form. The library is currently at version 0.5.2 and maintains an active development and release cadence, requiring Python >=3.10.

data ai-ml

pip install scikit-misc

github ↗package ↗homepage ↗docs ↗changelog ↗

INSTALL

IMPORT

SIG · SCIKIT-MISC

scikit-misc

datapythonv0.5.2

Install

3.7s avg

Import

359ms

Disk

94MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.5.2 · 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 0.287s · 86MB

94MB installed

● package 94MB

Code

Verified usage

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

loess

✓ from skmisc.loess import loess

This example demonstrates how to use the `loess` function from `skmisc.loess` to perform locally-weighted regression on a sample dataset, generating smoothed predictions.

import numpy as np from skmisc.loess import loess # Generate some sample data x = np.linspace(0, 10, 100) y = np.sin(x) + np.random.normal(0, 0.5, 100) # Fit a LOESS model model = loess(x, y) model.fit() # Get smoothed predictions and statistics pred = model.predict(x).values print(f"First 5 original y values: {y[:5]}") print(f"First 5 smoothed y values: {pred[:5]}")

Debug

Known issues

gotchaUsers often encounter 'ModuleNotFoundError' even after installation, particularly in complex environments like Anaconda or when using different Python interpreters.

fix

Ensure `pip install scikit-misc` or `conda install conda-forge::scikit-misc` is run within the *correct* active Python environment. If using Jupyter, ensure the kernel points to this environment. Sometimes, reinstalling with `--user` or explicitly managing `PYTHONPATH` might be necessary.

affects: All versions

breakingOlder SciPy users might recall algorithms (e.g., `scipy.misc.face`, `scipy.misc.ascent`) that were deprecated or removed from `scipy.misc`. `scikit-misc` aims to re-introduce some of these or similar functionalities, but direct `scipy.misc` imports will no longer work and require migrating to `scikit-misc` or other SciPy submodules.

fix

Update imports to `from skmisc.some_module import function` or use alternative functions within modern SciPy modules.

affects: Prior to scikit-misc's existence / SciPy versions that removed `scipy.misc`

gotchaBuilding `scikit-misc` from source, especially on less common platforms or with pre-release Python versions (e.g., Python 3.13, or sometimes on Windows), can lead to compilation errors due to dependencies on Fortran/C/Cython code and build system interactions with `numpy`.

fix

Use official pre-built wheels (`pip install scikit-misc`) whenever possible. For build failures, ensure `numpy` is installed as a build dependency, and check the GitHub issues for specific patches or workarounds related to your Python/Numpy version. Recent commits have addressed Python >= 3.12 and Numpy 2.0 compatibility.

affects: All versions (especially when building from source or with new Python/Numpy versions)

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'scikit-misc'

The `scikit-misc` package is either not installed or not installed in the Python environment currently in use.

fix

Run `pip install scikit-misc` or `conda install conda-forge::scikit-misc` in your active environment. Verify the environment is correctly activated.

For loess smoothing, install 'scikit-misc'

This error typically occurs when another library (e.g., `plotnine` or `scanpy`) tries to use `skmisc.loess` but cannot find the installed `scikit-misc` package in the execution environment.

fix

Confirm `scikit-misc` is installed in the *same* Python environment where the calling library is installed and being executed. Reinstalling `scikit-misc` with `pip install --user scikit-misc --force` or ensuring the correct `PYTHONPATH` can sometimes resolve this.

ERROR: Command `/usr/bin/python3 -c 'import os os.chdir(os.path.join("..", "tools")) import numpy as np try: incdir = os.path.relpath(np.get_include()) except Exception: incdir = np.get_include() print(incdir) '` failed with status 1.

This is a build-time error, often related to `meson-python` failing to find `numpy` headers during compilation, particularly with newer Python versions (e.g., 3.13) where build dependencies might not be automatically linked.

fix

This issue has been addressed in recent `scikit-misc` commits. Ensure you are using the latest version of `scikit-misc`. If building from source, ensure `numpy` is correctly installed in your build environment. Prefer installing pre-built wheels if available.

Upgrade

Version history

0.5.2latest on PyPI · released Nov 3, 2025

Audit

Dependencies

numpyrequiredFundamental for numerical operations; often a build dependency.

scipyrequiredOften used alongside SciPy; contains algorithms complementary to SciPy's core.

Agent activity

16 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/has2k1/scikit-misc ↗

packagehttps://pypi.org/project/scikit-misc/ ↗

homepagehttps://has2k1.github.io/scikit-misc/stable ↗

docshttps://has2k1.github.io/scikit-misc/stable ↗

changeloghttps://has2k1.github.io/scikit-misc/stable/changelog.html ↗