python大 pandas :按列A删除重复项,保持列B中值最大的行 [英] python pandas: Remove duplicates by columns A, keeping the row with the highest value in column B
问题描述
所以这样:
我有一个数据框,在A列中有重复值。我想删除重复项,保持列B中值最大的行。 / p>
AB
1 10
1 20
2 30
2 40
3 10
应该变成这样:
AB
1 20
2 40
3 10
Wes已经添加了一些很好的功能来删除重复项: http:// wesmckinney.com/blog/?p=340 。但是AFAICT是为了准确的重复设计,所以没有提到选择哪些行被保留的标准。
我猜想这可能是一个简单的方法, - 在删除重复项之前,可以像对数据框进行排序一样简单 - 但是我不知道groupby的内部逻辑能够很好地计算出来。任何建议?
这需要最后一个。不是最大值:
在[10]中:df.drop_duplicates(cols ='A',take_last = True)
出[10]:
AB
1 1 20
3 2 40
4 3 10
您还可以执行以下操作:
在[12]中:df。 groupby('A',group_keys = False).apply(lambda x:x.ix [xBidxmax()])
输出[12]:
AB
A
1 1 20
2 2 40
3 3 10
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?
This takes the last. Not the maximum though:
In [10]: df.drop_duplicates(cols='A', take_last=True)
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.ix[x.B.idxmax()])
Out[12]:
A B
A
1 1 20
2 2 40
3 3 10
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