What can skrub do?

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 more.

>>> 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.

Installation

skrub can easily be installed via pip or conda. For more installation information, see the installation instructions.

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 section.

To report a bug or suggest enhancements, please open an issue and/or submit a pull request.