How to find the index of a value by row in a dataframe in python and extract the value of the following column

Question

I have the following dataframe using pandas df = pd.DataFrame({'Last_Name': ['Smith', None, 'Brown'], 'Date0': ['01/01/1999','01/06/1999','01/01/1979'], 'Age0': [29,44,21], 'Date1': ['08/01/1999','07/01/2014','01/01/2016'],'Age1': [35, 45, 47], 'Date2': [None,'01/06/2035','08/01/1979'],'Age2': [47, None, 74], 'Last_age': [47,45,74]}) I would like to add new column to get the date corresponding to the value presents in 'Last_age' for each row to get something like that : df = pd.DataFrame({'Last_Name': ['Smith', None, 'Brown'], 'Date0': ['01/01/1999','01/06/1999','01/01/1979'], 'Age0': [29,44,21], 'Date1': ['08/01/1999','07/01/2014','01/01/2016'],'Age1': [35, 45, 47], 'Date2': [None,'01/06/2035','08/01/1979'],'Age2': [47, None, 74], 'Last_age': [47,45,74], 'Last_age_date': ['Error no date','07/01/2014','08/01/1979']}) JavaScript questions and answers, JavaScript questions pdf, JavaScript question bank, JavaScript questions and answers pdf, mcq on JavaScript pdf, JavaScript questions and solutions, JavaScript mcq Test , Interview JavaScript questions, JavaScript Questions for Interview, JavaScript MCQ (Multiple Choice Questions)

Answer 1

I will just using wide_to_long reshape your df s=pd.wide_to_long(df.reset_index(),['Date','Age'],i=['Last_age','index'],j='Drop') s.loc[s.Age==s.index.get_level_values(0),'Date'] Out[199]: Last_age index Drop 47 0 2 None 45 1 1 07/01/2014 74 2 2 08/01/1979 Name: Date, dtype: object df['Last_age_date']=s.loc[s.Age==s.index.get_level_values(0),'Date'].values df Out[201]: Last_Name Date0 Age0 ... Age2 Last_age Last_age_date 0 Smith 01/01/1999 29 ... 47.0 47 None 1 None 01/06/1999 44 ... NaN 45 07/01/2014 2 Brown 01/01/1979 21 ... 74.0 74 08/01/1979 [3 rows x 9 columns]