def psi(data_base, data_new, num_bins = 10):
    # Sort the data
    data_base = sorted(data_base)
    data_new = sorted(data_new)

    # Prepare the bins
    min_val = min(data_base[0], data_new[0])
    max_val = max(data_base[-1], data_new[-1])
    bins = [min_val + (max_val - min_val)*(i)/num_bins for i in range(num_bins+1)]
    bins[0] = min_val - 0.0001
    bins[-1] = max_val + 0.0001

    # Bucketize the baseline data and count the samples
    bins_base = pd.cut(data_base, bins = bins, labels = range(1,num_bins+1))
    df_base = pd.DataFrame({'base': data_base, 'bin': bins_base})
    grp_base = df_base.groupby('bin').count()
    grp_base['percent_base'] = grp_base['base'] / grp_base['base'].sum() 

    # Bucketize the new data and count the samples
    bins_new = pd.cut(data_new, bins = bins, labels = range(1,num_bins+1))
    df_new = pd.DataFrame({'new': data_new, 'bin': bins_new})
    grp_new = df_new.groupby('bin').count()
    grp_new['percent_new'] = grp_new['new'] / grp_new['new'].sum()

    # Compare the bins
    psi_df = grp_base.join(grp_new, on = "bin", how = "inner")

    # Calculate the PSI
    psi_df['percent_base'] = psi_df['percent_base'].replace(0, 0.0001)
    psi_df['percent_new'] = psi_df['percent_new'].replace(0, 0.0001)
    psi_df['psi'] = (psi_df['percent_base'] - psi_df['percent_new']) * np.log(psi_df['percent_base'] / psi_df['percent_new'])

    # Return the total PSI value
    return np.sum(psi_df['psi'].values)

# Conduct K-S Test for numeric feature
psi(original_df['Credit_History_Age'], new_df1['Credit_History_Age'])
psi(original_df['Credit_History_Age'], new_df2['Credit_History_Age'])
psi(original_df['Credit_History_Age'], new_df3['Credit_History_Age'])

# Conduct K-S Test for categorical feature
psi(original_df['Payment_Behaviour'], new_df1['Payment_Behaviour'])
psi(original_df['Payment_Behaviour'], new_df2['Payment_Behaviour'])
psi(original_df['Payment_Behaviour'], new_df3['Payment_Behaviour'])