pandas 集团的价值范围 [英] Pandas Groupby Range of Values

问题描述

是否有一种简单的方法可以在一系列值增量上调用 groupby ？例如，考虑到下面的例子，我可以使用 0.155 增量对列 B 进行分组和列分组，以便例如第一个列 B 中的几个组被分成 0，-0.155,0.155-0.31 ... p>

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

或者我可以先分类将这些增量数据放入新列中，然后使用 groupby 确定可能适用于列 A 的任何相关统计>

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?

推荐答案

您可能对 pd.cut ：

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]

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