python pandas:按A列删除重复项，保留B列中具有最高值的行

问题描述

我有一个在 A 列中有重复值的数据框.我想删除重复项，保留 B 列中具有最高值的行.

I have a dataframe with repeat values in column A. I want to drop duplicates, keeping the row with the highest value in column B.

所以:

A B
1 10
1 20
2 30
2 40
3 10

应该变成这样:

A B
1 20
2 40
3 10

Wes 添加了一些不错的功能来删除重复项:http://wesmckinney.com/blog/?p=340.但是 AFAICT，它是为完全重复而设计的，因此没有提及选择保留哪些行的标准.

Wes has added some nice functionality to drop duplicates: http://wesmckinney.com/blog/?p=340. But AFAICT, it's designed for exact duplicates, so there's no mention of criteria for selecting which rows get kept.

我猜可能有一种简单的方法可以做到这一点——可能就像在删除重复项之前对数据帧进行排序一样简单——但我不太了解 groupby 的内部逻辑，无法弄清楚.有什么建议吗?

I'm guessing there's probably an easy way to do this---maybe as easy as sorting the dataframe before dropping duplicates---but I don't know groupby's internal logic well enough to figure it out. Any suggestions?

推荐答案

这需要最后一个.虽然不是最大值:

This takes the last. Not the maximum though:

In [10]: df.drop_duplicates(subset='A', keep="last")
Out[10]:
   A   B
1  1  20
3  2  40
4  3  10

您还可以执行以下操作:

You can do also something like:

In [12]: df.groupby('A', group_keys=False).apply(lambda x: x.loc[x.B.idxmax()])
Out[12]:
   A   B
A
1  1  20
2  2  40
3  3  10

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