Pandas Groupby Range of Values

Is there an easy method in pandas to invoke groupby on a range of values increments? For instance given the example below can I bin and group column B with a 0.155 increment so that for example, the first couple of groups in column B are divided into ranges between 0, -0.155, 0.155 - 0.31 ...

    import numpy as np
    import pandas as pd
    df=pd.DataFrame({'A':np.random.random(20),'B':np.random.random(20)})

         A         B
    0  0.383493  0.250785
    1  0.572949  0.139555
    2  0.652391  0.401983
    3  0.214145  0.696935
    4  0.848551  0.516692

Alternatively I could first categorize the data by those increments into a new column and subsequently use groupby to determine any relevant statistics that may be applicable in column A?

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]

From: stackoverflow.com/q/21441259

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