python for loop, appending data from 12 columns to 1 column

Question

I am trying to determine what this loop does and if it can be done in a clearer way.

My understanding: For each iteration of the loop, it's taking one of the user ID fields(Col1 to Col 12) and writing that to the ID_final field. And then it tries to use that to join to "data" dataframe. Then appends everything to final_hr dataframe.

column_list = pd.DataFrame(['Col1','Col2','Col3','Col4', 'Col5',
'Col6','Col7','Col8','Col9','Col10',
'Col11','Col12'])

final_hr = pd.DataFrame()
 
for column in range(len(column_list)):
            hr_new=hr.copy()
          #Drop rows containing NAN for column 'col4' for new merge
          hr_new.dropna(subset=[column_list.iloc[column,0]], inplace = True)
          #Creating a new column for merge
           hr_new['ID_final']=hr_new[column_list.iloc[column,0]]
           #case folding
           hr_new['ID_final']=hr_new['ID_final'].str.strip().str.upper()
           #Merge data
          merged_data = pd.merge(hr_new, data, how='left', left_on='ID_final', right_on ='OtherID')
           #Concatinating all data together
            final_hr = final_hr.append(merged_data)
 
HR_attestation = final_hr.drop_duplicates()

I have trouble understanding how this exactly works.

In this output, i am unsure how the user "BADAWI6" from Col4 becomes the ID_Final. Why isnt Col1 or Col7 becoming the ID_Final here?