import pandas as pd dataframe = pd.DataFrame({'col_1': ['aaa', 'bbb', 'ccc', 'ddd'],   'col_2': ['name_aaa', 'name_bbb', 'name_ccc', 'name_ddd'],  'col_3': ['job_aaa', 'job_bbb', 'job_ccc', 'job_ddd']})  dataframe['featurs'] = dataframe.apply(lambda x: " ".join([j + " <%d>" % i for i, j in enumerate(x)]), axis=1) print(dataframe) 

Output

 col_1 col_2 col_3  featurs 0 aaa name_aaa job_aaa aaa <0> name_aaa <1> job_aaa <2> 1 bbb name_bbb job_bbb bbb <0> name_bbb <1> job_bbb <2> 2 ccc name_ccc job_ccc ccc <0> name_ccc <1> job_ccc <2> 3 ddd name_ddd job_ddd ddd <0> name_ddd <1> job_ddd <2> 

def join_(value): vals = []  for i, j in enumerate(value):  vals.append(j + " <%d>" % i if i < len(value) - 1 else j)  return " ".join(vals) # setting axis=1 will pass all columns to the join_ func. dataframe['featurs'] = dataframe.apply(lambda x: join_(x), axis=1) print(dataframe)   

Output

 col_1 col_2 col_3 featurs 0 aaa name_aaa job_aaa aaa <0> name_aaa <1> job_aaa 1 bbb name_bbb job_bbb bbb <0> name_bbb <1> job_bbb 2 ccc name_ccc job_ccc ccc <0> name_ccc <1> job_ccc 3 ddd name_ddd job_ddd ddd <0> name_ddd <1> job_ddd