pandas :选择groupby.sum()满足条件的行 [英] Pandas: Selecting rows for which groupby.sum() satisfies condition

问题描述

在熊猫中，我有一个以下形式的数据框:

In pandas I have a dataframe of the form:

>>> import pandas as pd  
>>> df = pd.DataFrame({'ID':[51,51,51,24,24,24,31], 'x':[0,1,0,0,1,1,0]})
>>> df

ID   x
51   0
51   1
51   0
24   0
24   1
24   1
31   0

对于每个"ID"，多次记录"x"的值，它是0或1.我想从df中选择那些包含"x"为1的"ID"的行.至少两次.

For every 'ID' the value of 'x' is recorded several times, it is either 0 or 1. I want to select those rows from df that contain an 'ID' for which 'x' is 1 at least twice.

对于每个"ID"，我设法计算"x"为1的次数，

For every 'ID' I manage to count the number of times 'x' is 1, by

>>> df.groupby('ID')['x'].sum()

ID
51    1
24    2
31    0

但是我不知道如何从这里继续.我想要以下输出:

But I don't know how to proceed from here. I would like the following output:

ID   x
24   0
24   1
24   1

推荐答案

使用groupby和filter

df.groupby('ID').filter(lambda s: s.x.sum()>=2)

输出:

   ID  x
3  24  0
4  24  1
5  24  1

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