Skrub is a library that eases machine learning with dataframes, from exploring dataframes to validating a machine-learning pipeline.
The |TableReport| is a powerful data exploration tool, which can be followed by data sanitization and feature engineering tools in the |Cleaner| and |TableVectorizer|. The |tabular_pipeline| combines the two to build a strong baseline for dataframes.
The skrub :ref:`column-level encoders<user_guide_encoders_index>` can be tweaked by the user for more specific needs. Various :ref:`multi-column transformers <user_guide_building_pipeline_index>` and the :ref:`selectors API<user_guide_selectors>` provide a high degree of control over which columns should be modified.
More complex, multi-table scenarios can make use of the skrub :ref:`Data Ops <user_guide_data_ops_index>`, which enable constructing and validating pipelines that involve multiple dataframes and hyperparameter tuning.
Skrub does not replace pandas or polars. Instead, it leverages the dataframe libraries to provide more high-level building blocks that perform the data preprocessing steps that are typically needed in a machine learning pipeline.
.. toctree:: :maxdepth: 3 exploring_a_dataframe default_wrangling column_level_featurizing multi_column_operations data_ops configuration_and_utils joining_dataframes