从数据框中删除重复项，基于两列 A，B，在另一列 C 中保持具有最大值的行

问题描述

我有一个 pandas 数据框，其中包含根据两列(A 和 B)的重复值:

I have a pandas dataframe which contains duplicates values according to two columns (A and B):

我想删除在 C 列中保持最大值的行的重复项.这将导致:

I want to remove duplicates keeping the row with max value in column C. This would lead to:

我不知道该怎么做.我应该使用 drop_duplicates() 吗?

I cannot figure out how to do that. Should I use drop_duplicates(), something else?

推荐答案

你可以使用 group by:

You can do it using group by:

c_maxes = df.groupby(['A', 'B']).C.transform(max)
df = df.loc[df.C == c_maxes]

c_maxes 是每个组中 C 最大值的Series，但长度和索引相同df.如果您还没有使用过 .transform，那么打印 c_maxes 可能是一个好主意，看看它是如何工作的.

c_maxes is a Series of the maximum values of C in each group but which is of the same length and with the same index as df. If you haven't used .transform then printing c_maxes might be a good idea to see how it works.

使用 drop_duplicates 的另一种方法是

Another approach using drop_duplicates would be

df.sort('C').drop_duplicates(subset=['A', 'B'], take_last=True)

不确定哪个更有效，但我猜是第一种方法，因为它不涉及排序.

Not sure which is more efficient but I guess the first approach as it doesn't involve sorting.

从 pandas 0.18 开始，第二个解决方案是

From pandas 0.18 up the second solution would be

df.sort_values('C').drop_duplicates(subset=['A', 'B'], keep='last')

或者，或者，

df.sort_values('C', ascending=False).drop_duplicates(subset=['A', 'B'])

无论如何，groupby 解决方案的性能似乎要好得多:

In any case, the groupby solution seems to be significantly more performing:

%timeit -n 10 df.loc[df.groupby(['A', 'B']).C.max == df.C]
10 loops, best of 3: 25.7 ms per loop

%timeit -n 10 df.sort_values('C').drop_duplicates(subset=['A', 'B'], keep='last')
10 loops, best of 3: 101 ms per loop