Pandas Groupby Range of Values

You might be interested in pd.cut:

>>> df.groupby(pd.cut(df["B"], np.arange(0, 1.0+0.155, 0.155))).sum()
                      A         B
B                                
(0, 0.155]     2.775458  0.246394
(0.155, 0.31]  1.123989  0.471618
(0.31, 0.465]  2.051814  1.882763
(0.465, 0.62]  2.277960  1.528492
(0.62, 0.775]  1.577419  2.810723
(0.775, 0.93]  0.535100  1.694955
(0.93, 1.085]       NaN       NaN

[7 rows x 2 columns]

Try this:

df = df.sort_values('B')
bins =  np.arange(0, 1.0, 0.155)
ind = np.digitize(df['B'], bins)
    
print df.groupby(ind).head() 

Of course you can use any function on the groups not just head.