{% extends "layout.html" %} {% set title = 'skrub: Machine Learning for dataframes' %} {%- block extrahead %} {{ super() }} {# Add here landing-page specific stuff that goes in the header (eg css) #} {%- endblock extrahead %} {% block docs_navbar %} {{ super() }} {# We add the full-width banner below the navbar, as the div there is still full-width (unlike the article) #}
skrub
is a Python library to
ease preprocessing and feature engineering for
tabular machine learning.
Our long-term goal is to directly connect database tables to machine learning estimators.
Create strong scikit-learn pipeline baselines effortlessly with
TableVectorizer
and
tabular_learner
.
Encode text and high cardinality categorical data with the
GapEncoder
and
MinHashEncoder
, and
extract features from dates with the
DatetimeEncoder
.
Explore your dataframes interactively with
TableReport
.
Click anywhere on the table
The Skrub project is powered by the efforts of a world-wide community of contributors. Here we display a randomly selected group of 30 contributors.
Ready to write less code and get more insights? Dive into skrub
now
and be part of an emerging community!