Pandas Groupby Range of Values
group-by, pandas, python
Solution
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]
Problem
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`?