### How to get the Weight of Evidence (WOE) and Information Value (IV) in Python/pandas?

Written by- Sharif 12390 times views

To calculate the Weight of Evidence (WOE) and Information Value (IV) in Python/pandas, you can use the woe() and iv() functions provided by the WeightOfEvidence class in the pywoe library.
Here is an example of how you can use these functions:
`import pandas as pdfrom pywoe.pywoe import WeightOfEvidence# Load the data into a pandas DataFramedf = pd.read_csv('data.csv')# Select the target column and the feature columnstarget_col = 'target'feature_cols = ['feature1', 'feature2', 'feature3']# Create a WeightOfEvidence objectwoe = WeightOfEvidence()# Calculate the WOE for each featurewoe_dict = woe.woe(df, target_col, feature_cols)# Calculate the IV for each featureiv_dict = woe.iv(df, target_col, feature_cols)# Print the WOE and IV for each featurefor feature, woe_val in woe_dict.items():    iv_val = iv_dict[feature]    print(f'Feature: {feature}, WOE: {woe_val}, IV: {iv_val}')`
Alternatively, you can also use the category_encoders library to calculate the WOE and IV. Here is an example of how you can do that:
`import pandas as pdimport category_encoders as ce# Load the data into a pandas DataFramedf = pd.read_csv('data.csv')# Select the target column and the feature columnstarget_col = 'target'feature_cols = ['feature1', 'feature2', 'feature3']# Create a WOE encoderencoder = ce.WOEEncoder(cols=feature_cols)# Fit the encoder on the dataencoder.fit(df[feature_cols], df[target_col])# Transform the data using the encoderdf_woe = encoder.transform(df[feature_cols])# Print the WOE and IV for each featurefor col, woe_val, iv_val in zip(df_woe.columns, encoder.woe_, encoder.iv_):    print(f'Feature: {col}, WOE: {woe_val}, IV: {iv_val}')`
I hope this helps! Let us know if you have any questions.