一次在多个列上使用pandas groupby().apply(list) [英] Using pandas groupby().apply(list) on multiple columns at once

问题描述

我正在尝试将一个数据帧的多行合并为一行，将具有不同值的列合并到一个列表中.有多个具有不同值的列.

I'm trying to combine multiple rows of a dataframe into one row, with the columns with different values being combined in a list. There are multiple columns with different values.

如果只需要对列表进行1列(在本例中为'b')，则 df.groupby('a')['b'].apply(list)效果很好，但我不知道如何针对多列进行该操作.

The df.groupby('a')['b'].apply(list) works well if only 1 column ('b' in this instance) has to be made to a list, but I can't figure out how to do it for multiple columns.

数据框:

   a  b  c       d
0  1  b  1   first
1  1  b  2  second
2  2  c  1   third
3  2  c  2  fourth
4  2  c  3   fifth

首选数据帧后操作:

   a  b          c                       d
0  1  b     [1, 2]         [first, second]
1  2  c  [1, 2, 3]  [third, fourth, fifth]

有一种简单的方法吗?

推荐答案

df = df.groupby(['a','b']).apply(lambda x: [list(x['c']), list(x['d'])]).apply(pd.Series)
df.columns =['a','b','c','d']

输出

   a  b          c                       d
0  1  b     [1, 2]         [first, second]
1  2  c  [1, 2, 3]  [third, fourth, fifth]

这篇关于一次在多个列上使用pandas groupby().apply(list)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IT屋！