python - Get average counts per minute by hour -

i have dataframe time stamp index , column of labels

df=dataframe({'time':[ datetime(2015,11,2,4,41,10),     datetime(2015,11,2,4,41,39), datetime(2015,11,2,4,41,47),  datetime(2015,11,2,4,41,59), datetime(2015,11,2,4,42,4),     datetime(2015,11,2,4,42,11), datetime(2015,11,2,4,42,15), datetime(2015,11,2,4,42,30),     datetime(2015,11,2,4,42,39),   datetime(2015,11,2,4,42,41),datetime(2015,11,2,5,2,9),datetime(2015,11,2,    5,2,10), datetime(2015,11,2,5,2,16),datetime(2015,11,2,5,2,29),datetime(2015,11,2,    5,2,51), datetime(2015,11,2,5,9,1),datetime(2015,11,2,5,9,21),datetime(2015,11,2,5,9,31), datetime(2015,11,2,5,9,40),datetime(2015,11,2,5,9,55)], 'label':[2,0,0,0,1,0,0,1,1,1,1,3,0,0,3,0,1,0,1,1]}).set_index(['time']) 

i want avergae number of times label appears in distinct minute in distnct hour.

for example, label 0 appears 3 times in hour 4 in minute 41, 2 times in hour 4 in minute 42,
2 times in hour 5 in in minute 2, , 2 times in hour 5 in minute 9 average count per minute in hour 4

(2+3)/2=2.5  

and count per minute in hour 5

(2+2)/2=2 

the output looking

hour 1 label  avg 0      2.5 1      2 2       .5 3       0   hour 2 label  avg 0      2 1      1.5 2      0 3      1 

what have far

df['hour']=df.index.hour  hour_grp=df.groupby(['hour'], as_index=false) 

then can deo

res=[] key, value in hour_grp:     res.append(value) 

then group minute

res[0].groupby(pd.timegrouper('1min'))['label'].value_counts() 

but i'm stuck, not mention not efficient

accessing minute of datetimeindex:

mn = df.index.minute 

accessing hour of datetimeindex:

hr = df.index.hour 

perform groupby keeping above obtained variables keys. compute value_counts of contents under label , unstack filling missing values 0. finally, average them across index-axis containing hour values.

df.groupby([mn,hr])['label'].value_counts().unstack(fill_value=0).mean(level=1)