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After dividing the variable into boxes, you need to calculate the importance of the variable. IV is one of the statistics to evaluate the differentiation or importance of the variable. The code for python to calculate the IV value is as follows:

Def CalcIV (Xvar Yvar): np.sum (Yvar==0) Numb1 = np.sum (Yvar==1) N_0_group = np.zeros (np.unique (Xvar) .shape) N_1_group = np.zeros (np.unique (Xvar) .shape) for i in range (len (np.unique (Xvar): N_0_group [I] = Yvar [(Xvar = = np.unique (Xvar) [I]) & (Yvar==0)]. Count () Numb1Group [I] = Yvar [(Xvar = = np.unique (Xvar) [I]) & (Yvar = = 1)] .count () iv = np.sum ((N_0_group/N_0-N_1_group/N_1) * np.log ((N_0_group/N_0) / (N_1_group/N_1)) return iv def caliv_batch (df) Kvar, Yvar): df_Xvar = df.drop ([Kvar, Yvar], axis=1) ivlist = [] for col in df_Xvar.columns: iv = CalcIV (df [col], df [Yvar]) ivlist.append (iv) names = list (df_Xvar.columns) iv_df = pd.DataFrame ({'Var': names,' Iv': ivlist}, columns= ['Var',' Iv']) return iv_df

Where df is the dataset after the box, Kvar is the primary key, and Yvar is the y variable (0 is good, 1 is bad). The running result of the code is as follows:

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