更新时间:2023-02-02 22:19:36
您可以使用分组依据:
c_maxes = df.groupby(['A', 'B']).C.transform(max)
df = df.loc[df.C == c_maxes]
c_maxes
是每个组中C
的最大值的Series
,但长度与df
相同且具有相同的索引.如果您未使用.transform
,则打印c_maxes
可能是一个很好的主意,以了解其工作原理.
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
df.sort('C').drop_duplicates(subset=['A', 'B'], take_last=True)
不确定哪种方法更有效,但是我猜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,第二个解决方案将是
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'])
在任何情况下,groupby
解决方案的性能似乎都明显更高:
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