API Reference#
Complete reference for all Gators transformers, organized by functionality.
Overview#
Gators provides 75+ transformers across 11 categories, all with a consistent sklearn-compatible API.
Each transformer implements .fit() and .transform() methods and works seamlessly with Polars DataFrames.
Data Cleaning#
Quality filters, variance detection, correlation removal, and data quality transformations.
Clippers#
Outlier detection and clipping strategies including Gaussian, IQR, MAD, and Quantile methods.
Encoders#
Categorical encoding methods: OneHot, Target, WOE, CatBoost, Ordinal, and more.
Feature Generation#
Create numeric features from existing columns: polynomial, ratios, aggregations, and custom transformations.
String Features#
Extract information from text: length, patterns, n-grams, substring extraction, and text statistics.
DateTime Features#
Temporal feature engineering: cyclic encoding, holidays, business hours, time windows, and date differences.
Imputers#
Handle missing values with mean, median, mode, constant, or group-based imputation strategies.
Discretizers#
Bin continuous variables using equal-width, quantile, k-means, tree-based, or custom strategies.
Scalers#
Normalize and transform features: StandardScaler, MinMaxScaler, Box-Cox, Yeo-Johnson, and more.
Pipeline#
Chain multiple transformers together for streamlined preprocessing workflows.
Feature Selection#
Select important features using Information Value, Feature Stability Index, and other metrics.