python pandas: Remove duplicates by columns A, keeping the row with the highest value in column B

Question

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.

So this:

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

Should turn into this:

A B
1 20
2 40
3 10

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.

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?

Answer 1

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

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