Welcome to the pybear documentation#

pybear is a scikit-learn-style Python computing library that augments data analytics functionality found in popular packages like scikit-learn and xgboost.

Check out the installation section for information on how to install pybear.

See the Usage section for ideas about when and where to use pybear.

See the API section for further information on the available modules.

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Mission Statement#

pybear seeks to add to and/or enhance existing functionality in the Python data analytics ecosystem.

pybear runs on all actively supported versions of Python.

pybear must seamlessly integrate into conventional Python data analytics workflows. The way pybear does this is by using the scikit-learn API.

Every pybear estimator/transformer seeks to fulfill 4 objectives:

  1. handle missing data: robust handling of all nan-like values (numpy.nan, pandas.NA, etc.)

  2. fast processing with parallelism: use joblib when there is benefit

  3. bigger than memory data: every module is capable of incremental learning

  4. accept all common containers: accepts numpy, pandas, polars, and scipy sparse containers.

Contents#