python - Loop over groups Pandas Dataframe and get sum/count -

i using pandas structure , process data. dataframe:

and code enabled me dataframe:

(data[['time_bucket', 'beginning_time', 'bitrate', 2, 3]].groupby(['time_bucket', 'beginning_time', 2, 3])).aggregate(np.mean) 

now want have sum (ideally, sum , count) of 'bitrates' grouped in same time_bucket. example, first time_bucket((2016-07-08 02:00:00, 2016-07-08 02:05:00), must 93750000 sum , 25 count, case 'bitrate'.

i did :

data[['time_bucket', 'bitrate']].groupby(['time_bucket']).agg(['sum', 'count']) 

and result :

but want have data in 1 dataframe.

can simple loop on 'time_bucket' , apply function calculate sum of bitrates ? ideas ? thx !

i think need merge, need same levels of indexes of both dataframes, use reset_index. last original multiindex set_index:

data = pd.dataframe({'a':[1,1,1,1,1,1],                      'b':[4,4,4,5,5,5],                      'c':[3,3,3,1,1,1],                      'd':[1,3,1,3,1,3],                      'e':[5,3,6,5,7,1]})  print (data)     b  c  d  e 0  1  4  3  1  5 1  1  4  3  3  3 2  1  4  3  1  6 3  1  5  1  3  5 4  1  5  1  1  7 5  1  5  1  3  1 

df1 = data[['a', 'b', 'c', 'd','e']].groupby(['a', 'b', 'c', 'd']).aggregate(np.mean) print (df1)            e b c d      1 4 3 1  5.5       3  3.0   5 1 1  7.0       3  3.0  df2 = data[['a', 'c']].groupby(['a'])['c'].agg(['sum', 'count']) print (df2)    sum  count             1   12      6  print (pd.merge(df1.reset_index(['b','c','d']), df2, left_index=true, right_index=true)          .set_index(['b','c','d'], append=true))             e  sum  count b c d                  1 4 3 1  5.5   12      6       3  3.0   12      6   5 1 1  7.0   12      6       3  3.0   12      6 

i try solution output df1, aggregated impossible right data. if sum level c, 8 instead 12.