# Pandas Groupby Range of Values

## Question:

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`

?

## Answers:

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`

.

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]
```

so this is how I use the groupby function

```
df1=data
bins = [0,40,50,60,70,100]
group_names=['F','S','C','B','A']
df1['grade']=pd.cut(data['student_mark'],bins,labels=group_names)
df1
```