Python Pandas - 在 groupby 后过滤行 [英] Python pandas - filter rows after groupby

问题描述

例如我有下表:

index,A,B
0,0,0
1,0,8
2,0,8
3,1,0
4,1,5

按A分组后:

0:
index,A,B
0,0,0
1,0,8
2,0,8

1:
index,A,B
3,1,5
4,1,3

我需要的是从每个组中删除行，其中 B 列中的数字小于组列 B 中所有行的最大值.好吧，我在将这个问题翻译和表述为英语时遇到了问题，所以这里是示例:

What I need is to drop rows from each group, where the number in column B is less than maximum value from all rows from group's column B. Well I have a problem translating and formulating this problem to English so here is the example:

0 组中 B 列的行的最大值:8

Maximum value from rows in column B in group 0: 8

所以我想删除索引为 0 的行并保留索引为 1 和 2

So I want to drop row with index 0 and keep rows with indexes 1 and 2

组1中B列的行的最大值:5

所以我想删除索引为 4 的行并保留索引为 3

So I want to drop row with index 4 and keep row with index 3

我曾尝试使用pandas过滤功能，但问题是它一次对组中的所有行进行操作:

I have tried to use pandas filter function, but the problem is that it is operating on all rows in group at one time:

data = <example table>
grouped = data.groupby("A")
filtered = grouped.filter(lambda x: x["B"] == x["B"].max())

所以我理想情况下需要的是一些过滤器，它遍历组中的所有行.

感谢您的帮助！

附言有没有办法只删除组中的行而不返回DataFrame对象?

P.S. Is there also way to only delete rows in groups and do not return DataFrame object?

推荐答案

您只需要在 groupby 对象上使用 apply 即可.我修改了您的示例数据以使其更加清晰:

You just need to use apply on the groupby object. I modified your example data to make this a little more clear:

import pandas
from io import StringIO

csv = StringIO("""index,A,B
0,1,0.0
1,1,3.0
2,1,6.0
3,2,0.0
4,2,5.0
5,2,7.0""")

df = pandas.read_csv(csv, index_col='index')
groups = df.groupby(by=['A'])
print(groups.apply(lambda g: g[g['B'] == g['B'].max()]))

打印:

         A  B
A index      
1 2      1  6
2 4      2  7

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