如何组合 pandas 中的重复行? [英] How to combine duplicate rows in pandas?

问题描述

如何合并pandas中的重复行，填补缺失值?

How to combine duplicate rows in pandas, filling in missing values?

在下面的示例中，某些行在 c1 列中有缺失值，但 c2 列有重复项，可以用作索引来查找和填充在那些缺失值中.

In the example below, some rows have missing values in the c1 column, but the c2 column has duplicates that can be used as an index to look up and fill in those missing values.

输入数据如下所示:

    c1  c2
id      
0   10.0    a
1   NaN     b
2   30.0    c
3   10.0    a
4   20.0    b
5   NaN     c

所需的输出:

    c1  c2
0   10  a
1   20  b
2   30  c

但是如何做到这一点?

这是生成示例数据的代码:

Here is the code to generate the example data:

import pandas as pd
df = pd.DataFrame({
    'c1': [10, float('nan'), 30, 10, 20, float('nan')]
    'c2': [100, 200, 300, 100, 200, 300],
})

推荐答案

我认为需要 sort_values 和 drop_duplicates:

I think need sort_values with drop_duplicates:

df = df.sort_values(['c1','c2']).drop_duplicates(['c2'])
print (df)
     c1   c2
0  10.0  100
4  20.0  200
2  30.0  300

或者首先通过 NaN 的行="nofollow noreferrer">dropna:

Or first remove rows with NaNs by dropna:

df = df.dropna(subset=['c1']).drop_duplicates(['c2'])
print (df)
     c1   c2
0  10.0  100
2  30.0  300
4  20.0  200

<小时>

df = df.dropna(subset=['c1']).drop_duplicates(['c1','c2'])
print (df)
     c1   c2
0  10.0  100
2  30.0  300
4  20.0  200

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