Generalized linear models (GLMs) are interpretable, relatively quick to train, and specifying them helps the modeler understand the main effects in the data. This makes them a popular choice today to complement other machine-learning approaches. glum was conceived with the aim of offering the community an efficient, feature-rich, and Python-first GLM library with a scikit-learn-style API. More recently, we are striving to keep up with PyData community's ongoing push for dataframe-agnosticism.
While glum was originally heavily based on pandas, with the help of narwhals, we are close to being able to fit models on any dataset that the latter supports. This talk presents our experiences with achieving this goal.