|App_Flag|ATL_Flag|Cust_No|month1|month2
| 0      |  TV    | 1     | 1    | 0
| 0      |  FB    | 1     | 0    | 0
| 0      |  OOH   | 1     | 1    | 1
| 1      |  RAD   | 2     | 1    | 1
| 1      |  TV    | 2     | 1    | 0
| 1      |  FB    | 2     | 1    | 0

def str_sum(channel):
    return '>'.join(channel['c_path'])

wrk_data_temp = pd.melt(work_data_temp[['cust_no', 'ATL_Flag', 'max_exp_1_mnth', 'max_exp_2_mnth']], id_vars=['cust_no', 'ATL_Flag'], value_vars=['max_exp_1_mnth', 'max_exp_2_mnth'], value_name='key')
wrk_data_temp['variable'] = wrk_data_temp['variable'].str.extract(r'([d]+)').astype(int)
wrk_data_temp['c_path'] = wrk_data_temp.sort_values(['cust_no', 'variable', 'ATL_Flag'])[wrk_data_temp.key == 1][['cust_no', 'ATL_Flag', 'variable']].groupby(['cust_no', 'variable']).transform('sum')
wrk_data_temp2 = wrk_data_temp[['cust_no', 'variable', 'c_path']].drop_duplicates()
wrk_data_temp3 = wrk_data_temp2.dropna()
final = pd.DataFrame(wrk_data_temp3[['cust_no', 'c_path']].groupby('cust_no').apply(str_sum))