Python Pandas Groupby 值范围
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Pandas Groupby Range of Values
提问by BJEBN
Is there an easy method in pandas to invoke groupbyon a range of values increments? For instance given the example below can I bin and group column Bwith a 0.155increment so that for example, the first couple of groups in column Bare divided into ranges between '0 - 0.155, 0.155 - 0.31 ...`
在 Pandas 中是否有一种简单的方法来调用groupby一系列值增量？例如，给出下面的示例，我可以B使用0.155增量对列进行分组和分组，例如，列B中的前几个组被划分为“0 - 0.155、0.155 - 0.31 ...”之间的范围
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 groupbyto determine any relevant statistics that may be applicable in column A?
或者，我可以首先按这些增量将数据分类到一个新列中，然后用于groupby确定可能适用于列的任何相关统计数据A？
采纳答案by DSM
You might be interested in pd.cut:
您可能对以下内容感兴趣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]
回答by Alvaro Fuentes
Try this:
尝试这个：
df = df.sort('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.
当然，您可以对组使用任何功能，而不仅仅是head.
