Python calculates the IV value and uses

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After the variable is split, the importance of the variable is calculated, and IV is one of the statistics that evaluates the variable's degree of sensitivity or importance, and the code for Python to calculate the IV value is as follows:

def CalcIV(Xvar, Yvar):    N_0  = np.sum(Yvar==0)   N_1 = 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()       N_1_group[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 data set after the bins, KVAR is the primary key, Yvar is the y variable (0 is good, 1 is bad). The result of the code operation is as follows:

Python calculates the IV value and uses