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 | 您所在的位置:网站首页 › dataframe去重复行 › 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 |
本文介绍了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的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
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这篇关于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的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持吉威生活! [英文标题]Remove duplicates from dataframe, based on two columns A,B, keeping row with max value in another column C 声明:本媒体部分图片、文章来源于网络,版权归原作者所有,如有侵权,请联系QQ:330946442删除。 |
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