I got a bunch of only nans in the lecture about creating a scorecard. min_score=300 max_score=850 In [131]: min_score min_score=scorecard_df.groupby(['Original_names'])['Coef'].min() min_score Out[131]: Original_names Intercept -1.286588 acc_now_delinq 0.000000 addr_state 0.000000 annual_inc -0.068937 dti 0.000000 emp_length 0.000000 grade 0.000000 home_ownership 0.000000 initial_list_status 0.000000 inq_last_6mths 0.000000 int_rate 0.000000 mths_since_earliest_cr_line 0.000000 mths_since_issue_d -0.060627 mths_since_last_delinq 0.000000 mths_since_last_record -0.049872 purpose 0.000000 term 0.000000 verification_status -0.010562 Name: Coef, dtype: float64 In [130]: min_score_sum min_score_sum=scorecard_df.groupby(['Original_names'])['Coef'].min().sum() min_score_sum Out[130]: -1.4765851105053986 In [125]: max_score max_score=scorecard_df.groupby(['Original_names'])['Coef'].max() max_score Out[125]: Original_names Intercept -1.286588 acc_now_delinq 0.235443 addr_state 0.524189 annual_inc 0.577045 dti 0.389218 emp_length 0.126174 grade 0.912940 home_ownership 0.106218 initial_list_status 0.054820 inq_last_6mths 0.694467 int_rate 0.867411 mths_since_earliest_cr_line 0.131432 mths_since_issue_d 1.091989 mths_since_last_delinq 0.239036 mths_since_last_record 0.286678 purpose 0.300414 term 0.078659 verification_status 0.084432 Name: Coef, dtype: float64 In [126]: ore_sum max_score_sum=scorecard_df.groupby(['Original_names'])['Coef'].max().sum() max_score_sum Out[126]: 5.413976291458666 In [127]: _df scorecard_df['Score']=scorecard_df['Coef']*(max_score-min_score)/(max_score_sum-min_score_sum) In [132]: scorecard_df.Score.unique() Out[132]: array([nan])

Hi there, Are you sure that you indexes are correct? I would be easiest if you provide your notebook so we can examine it. Best, Iliya

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Last answered:

07 Apr 2020

Posted on:

06 Apr 2020

Scorecard

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Posted on:

07 Apr 2020