VIF utility function for checking multicollinearity with stats models

print_vif.py

import warnings
import statsmodels.api as sm
from statsmodels.stats.outliers_influence import variance_inflation_factor


def print_vif(x):
    """Utility for checking multicollinearity assumption
    
    :param x: input features to check using VIF. This is assumed to be a pandas.DataFrame
    :return: nothing is returned the VIFs are printed as a pandas series
    """
    # Silence numpy FutureWarning about .ptp
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        x = sm.add_constant(x)
    
    vifs = []
    for i in range(x.shape[1]):
        vif = variance_inflation_factor(x.values, i)
        vifs.append(vif)

    print('VIF results\n-------------------------------')
    print(pd.Series(vifs, index=x.columns))
    print('-------------------------------\n')