Unable to fill missing values with column value across all columns

Question

I have a dataframe like as shown below

df = pd.DataFrame({'Credit_History':['Yes','ABC','DEF', 'JKL'],
                   'Loan_Status':['T1','T2',np.nan,np.nan],
                   'subject_status':['DUMMA','CHUMMA',np.nan,np.nan],
                   'test_status':['test',np.nan,np.nan,np.nan]})

My objective is to fill the missing values with the corresponding credit_history value across all rows and columns

I tried the below but it doesn't work

cols = ['Loan_Status','subject_status','test_status']
df[cols] = df[cols].fillna(df['Credit_History'])

I expect my output to be like as shown below. Basically, whichever row is missing, it should pick the corresponding value from credit_history column

Answer 1

Use DataFrame.apply, so is used Series.fillna:

cols = ['Loan_Status','subject_status','test_status']
df[cols] = df[cols].apply(lambda x: x.fillna(df['Credit_History']))

print (df)
  Credit_History Loan_Status subject_status test_status
0            Yes          T1          DUMMA        test
1            ABC          T2         CHUMMA         ABC
2            DEF         DEF            DEF         DEF
3            JKL         JKL            JKL         JKL

Or with transpose:

cols = ['Loan_Status','subject_status','test_status']
df[cols] = df[cols].T.fillna(df['Credit_History']).T

print (df)
  Credit_History Loan_Status subject_status test_status
0            Yes          T1          DUMMA        test
1            ABC          T2         CHUMMA         ABC
2            DEF         DEF            DEF         DEF
3            JKL         JKL            JKL         JKL