Skrub
Machine learning with dataframes
Install / Use
/learn @skrub-data/SkrubREADME
skrub
.. image:: https://skrub-data.github.io/stable/_static/skrub.svg :align: center :width: 50 % :alt: skrub logo
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.. |py_ver| image:: https://img.shields.io/pypi/pyversions/skrub .. |pypi_var| image:: https://img.shields.io/pypi/v/skrub?color=informational .. |pypi_dl| image:: https://img.shields.io/pypi/dm/skrub .. |codecov| image:: https://img.shields.io/codecov/c/github/skrub-data/skrub/main .. |circleci| image:: https://img.shields.io/circleci/build/github/skrub-data/skrub/main?label=CircleCI .. |black| image:: https://img.shields.io/badge/code%20style-black-000000.svg
skrub (formerly dirty_cat) is a Python library that facilitates machine learning with dataframes.
If you like the package, spread the word and ⭐ this repository!
You can also join the Discord server <https://discord.gg/ABaPnm7fDC>_.
Website: https://skrub-data.org/
See our examples <https://skrub-data.org/stable/auto_examples>, or check out
the learning materials <https://skrub-data.org/skrub-materials/index.html>.
Installation
skrub can easily be installed via pip or conda. For more installation information, see
the installation instructions <https://skrub-data.org/stable/install.html>_.
Contributing
The best way to support the development of skrub is to spread the word!
Also, if you already are a skrub user, we would love to hear about your use cases and challenges in the Discussions <https://github.com/skrub-data/skrub/discussions>_ section.
To report a bug or suggest enhancements, please
open an issue <https://docs.github.com/en/issues/tracking-your-work-with-issues/creating-an-issue>_.
If you want to contribute directly to the library, then check the
how to contribute <https://skrub-data.org/stable/CONTRIBUTING.html>_ page on
the website for more information.
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