skrub (formerly dirty_cat) is a Python library that facilitates prepping your tables for machine learning.
If you like the package, spread the word and ⭐ this repository! You can also join the discord server.
Website: https://skrub-data.org/
The goal of skrub is to bridge the gap between tabular data sources and machine-learning models.
skrub provides high-level tools for joining dataframes (Joiner, AggJoiner, ...),
encoding columns (MinHashEncoder, ToCategorical, ...), building a pipeline
(TableVectorizer, tabular_learner, ...), and exploring interactively your data (TableReport).
>>> from skrub.datasets import fetch_employee_salaries
>>> dataset = fetch_employee_salaries()
>>> df = dataset.X
>>> y = dataset.y
>>> df.iloc[0]
gender F
department POL
department_name Department of Police
division MSB Information Mgmt and Tech Division Records...
assignment_category Fulltime-Regular
employee_position_title Office Services Coordinator
date_first_hired 09/22/1986
year_first_hired 1986>>> from sklearn.model_selection import cross_val_score
>>> from skrub import tabular_learner
>>> cross_val_score(tabular_learner('regressor'), df, y)
array([0.89370447, 0.89279068, 0.92282557, 0.92319094, 0.92162666])See our examples.
skrub can easily be installed via pip or conda. For more installation information, see
the installation instructions.
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 section.
To report a bug or suggest enhancements, please open an issue.
If you want to contribute directly to the library, then check the how to contribute page on the website for more information.