# Pandas Groupby Range of Values

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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]
``````

Try this:

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

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