optbinning

import numpy as np import pandas as pd from optbinning import OptimalBinning # Create dummy data np.random.seed(42) X = pd.DataFrame({ 'feature_1': np.random.rand(100) * 100, 'feature_2': np.random.randint(0, 5, 100), 'feature_3': np.random.normal(50, 10, 100), }) y = np.random.randint(0, 2, 100) # Binary target # Initialize and fit OptimalBinning for a continuous feature optb_num = OptimalBinning(name="feature_1", dtype="numerical", dtype_target="binary") optb_num.fit(X["feature_1"], y) # Transform the feature X["feature_1_binned"] = optb_num.transform(X["feature_1"]) # Print binning table print(f"Binning Table for feature_1:\n{optb_num.binning_table.build()}\n") # Example with a categorical feature optb_cat = OptimalBinning(name="feature_2", dtype="categorical", dtype_target="binary") optb_cat.fit(X["feature_2"], y) X["feature_2_binned"] = optb_cat.transform(X["feature_2"]) print(f"Binning Table for feature_2:\n{optb_cat.binning_table.build()}\n") # The transformed data print("Transformed DataFrame head:") print(X.head())