Dear 365Team,
 
I am referring to module “Credit Risk Modeling”, course “PD model estimation” and Video “PD model estimation”.
 
If I use this code
inputs_train_with_ref_cat = loan_data_inputs_train.loc[: , [‘grade:A’, ‘grade:B’, ‘grade:C’, … ]
then I get error message
“KeyError: ‘Passing list-likes to .loc or [] with any missing labels is no longer supported, see https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike’
 
So, I followed the recommendation to change .iloc with .reindex
(Source: https://365datascience.com/question/credir-risk-modeling-in-python-section-pd-model-6-2/)
inputs_train_with_ref_cat = loan_data_inputs_train.reindex([‘grade:A’, ‘grade:B’, ‘grade:C’, …], axis = 1)
 
But the problem is I get if I conduct this code
inputs_train = inputs_train_with_ref_cat.drop(ref_categories, axis = 1)
inputs_train.head()
many NaN in dataset inputs_train.
 
Where is man NaN in inputs_train.isnull().sum()

grade:A                                     0
grade:B                                     0
grade:C                                     0
grade:D                                     0
grade:E                                     0
grade:F                                     0
home_ownership:OWN                          0
home_ownership:MORTGAGE                     0
addr_state:NM_VA                       373028
addr_state:NY                               0
addr_state:OK_TN_MO_LA_MD_NC           373028
addr_state:CA                               0
addr_state:UT_KY_AZ_NJ                 373028
addr_state:AR_MI_PA_OH_MN              373028
addr_state:RI_MA_DE_SD_IN              373028
addr_state:GA_WA_OR                    373028
addr_state:WI_MT                       373028
addr_state:TX                               0
addr_state:IL_CT                       373028
addr_state:KS_SC_CO_VT_AK_MS           373028
addr_state:WV_NH_WY_DC_ME_ID           373028
verification_status:Not Verified            0
verification_status:Source Verified         0
purpose:credit_card                         0
purpose:debt_consolidation                  0
purpose:oth__med__vacation             373028
purpose:major_purch__car__home_impr    373028
initial_list_status:w                       0
term:36                                373028
emp_length:1                           373028
emp_length:2-4                         373028
emp_length:5-6                         373028
emp_length:7-9                         373028
emp_length:10                          373028
mths_since_issue_d:<38                 373028
mths_since_issue_d:38-39               373028
mths_since_issue_d:40-41               373028
mths_since_issue_d:42-48               373028
mths_since_issue_d:49-52               373028
mths_since_issue_d:53-64               373028
mths_since_issue_d:65-84               373028
int_rate:<9.548                        373028
int_rate:9.548-12.025                  373028
int_rate:12.025-15.74                  373028
int_rate:15.74-20.281                  373028
mths_since_earliest_cr_line:141-164    373028
mths_since_earliest_cr_line:165-247    373028
mths_since_earliest_cr_line:248-270    373028
mths_since_earliest_cr_line:271-352    373028
mths_since_earliest_cr_line:>352       373028
delinq_2yrs:0                          373028
delinq_2yrs:1-3                        373028
inq_last_6mths:0                       373028
inq_last_6mths:1-2                     373028
inq_last_6mths:3-6                     373028
open_acc:1-3                           373028
open_acc:4-12                          373028
open_acc:13-17                         373028
open_acc:18-22                         373028
open_acc:23-25                         373028
open_acc:26-30                         373028
open_acc:>=31                          373028
pub_rec:3-4                            373028
pub_rec:>=5                            373028
total_acc:28-51                        373028
total_acc:>=52                         373028
acc_now_delinq:>=1                     373028
total_rev_hi_lim:5K-10K                373028
total_rev_hi_lim:10K-20K               373028
total_rev_hi_lim:20K-30K               373028
total_rev_hi_lim:30K-40K               373028
total_rev_hi_lim:40K-55K               373028
total_rev_hi_lim:55K-95K               373028
total_rev_hi_lim:>95K                  373028
annual_inc:20K-30K                     373028
annual_inc:30K-40K                     373028
annual_inc:40K-50K                     373028
annual_inc:50K-60K                     373028
annual_inc:60K-70K                     373028
annual_inc:70K-80K                     373028
annual_inc:80K-90K                     373028
annual_inc:90K-100K                    373028
annual_inc:100K-120K                   373028
annual_inc:120K-140K                   373028
annual_inc:>140K                       373028
dti:<=1.4                              373028
dti:1.4-3.5                            373028
dti:3.5-7.7                            373028
dti:7.7-10.5                           373028
dti:10.5-16.1                          373028
dti:16.1-20.3                          373028
dti:20.3-21.7                          373028
dti:21.7-22.4                          373028
dti:22.4-35                            373028
mths_since_last_delinq:Missing         373028
mths_since_last_delinq:4-30            373028
mths_since_last_delinq:31-56           373028
mths_since_last_delinq:>=57            373028
mths_since_last_record:Missing         373028
mths_since_last_record:3-20            373028
mths_since_last_record:21-31           373028
mths_since_last_record:32-80           373028
mths_since_last_record:81-86           373028
mths_since_last_record:>=86            373028
dtype: int64

 
So I decided to look in dataset
loan_data_inputs_train.csv
loan_data_targets_train.csv
loan_data_inputs_test.csv
loan_data_targets_test.csv
and test each of its with .isnull().sum(). There is on NaN in dataset.
 
I have now no clue what should I do now? Where is the mistake, which i did? I hope I described the problem in clearly way.
Best regard
Volkmar