Creating multiple observations from single observation depending on conditions

Question

I have written a function in python with three loops which is time consuming. Is it possible to do the same operation in less time with some other way. Here are my code and sample data you can run at your end

#Expand files to minute-level
def expand_b34(data):
    final_list = []
    df_columns = list(data.columns.tolist())
    for i in range(len(data)):
      #print i
      row_list = np.repeat(data.values[i][np.newaxis,:],     data['DURATION'].loc[i], axis=0).tolist()
      start_end_list = range(data['START'].loc[i], data['END_MINUTE'].loc[i])
      for j in range(len(row_list)):
        row_list[j].extend([start_end_list[j]])
      for k in row_list:
        final_list.append(k)

   data = pd.DataFrame(final_list)
   data.columns = df_columns + ['START_MINUTE']
   data = data.drop(['START','END_MINUTE'], axis=1)

   return data               

df = expand_b34(test)

Sample data
date  Id     LD GOOD_AP_ORIG ap_station JULIAN_DAY START DURATION END_MINUTE PLDS PL PLT PLAY
16080 4012007 1 G            5000       16081       0       60       60      0    0   0  
16080 4012007 1 G            5000       16081      60       60      120      0    0   0  
16080 4012007 1 G            5000       16081     120       60      180      0    0   0  
16080 4012007 1 G            5000       16080     180       60      240      0    0   0  
16080 4012007 1 G            5000       16080     240      120      360      0    0   0  

Code Work flow:

1. calculate difference between START and END_MINUTE
2. expand the observations for the difference

So if the difference is 10, ten more lines of observations will be generated for the observation which is having the same data except for the START_MINUTE variable. START_MINUTE starts at 0 (because for that particular observation START == 0) and ends at 9.

For a single observation the loop creates 10 duplicate observations except for the start minute variable, which varies from 0 to 9.

Can someone help me to do the same observation expansion by optimizing my code?