Replacing The Weight-of-evidence (woe) With Its Corresponding Value

I have variable called x whose minimum value is zero and maximum is 2 million. So I cut the value into bins like this code: bins = [0,1,10000,20000,50000,60000,70000,100000,2000000

Solution 1:

I think you need cut with parameter labels and for replace misisng value is necessary add cat.add_categories before replace:

df_input = pd.DataFrame({'X':[0,20,100, 10000, 30000, 1000000]})

b = [-np.inf, 100, 10000, 20000, 50000]
l = ['-0.157688', '-0.083307', '0.381819', '0.364365']

df_input['X_WOE'] = pd.cut(df_input['X'], bins=b, labels=l,right=False)
df_input['X_WOE'] = df_input['X_WOE'].cat.add_categories(['0']).fillna('0')
print (df_input)
         X      X_WOE
0        0  -0.157688
1       20  -0.157688
2      100  -0.083307
3    10000   0.381819
4    30000   0.364365
5  1000000          0