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):
A B C
1 2 1
1 2 4
2 7 1
3 4 0
3 4 8
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:
A B C
1 2 4
2 7 1
3 4 8
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
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