Pandas Groupby 值范围 [英] Pandas Groupby Range of Values

问题描述

在 Pandas 中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，给出下面的示例，我可以使用 0.155 增量对 B 列进行分组和分组，例如，B 列中的前几个组> 分为 '0 - 0.155, 0.155 - 0.31 ...`

将 numpy 导入为 np将熊猫导入为 pddf=pd.DataFrame({'A':np.random.random(20),'B':np.random.random(20)})甲乙0 0.383493 0.2507851 0.572949 0.1395552 0.652391 0.4019833 0.214145 0.6969354 0.848551 0.516692

或者，我可以首先按这些增量将数据分类到一个新列中，然后使用 groupby 来确定任何可能适用于 A 列的相关统计数据?

解决方案

您可能对 pd.cut:

<预><代码>>>>df.groupby(pd.cut(df["B"], np.arange(0, 1.0+0.155, 0.155))).sum()甲乙乙(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 行 x 2 列]

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?

解决方案

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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