合并并合并2列不同的数据框

Question

我有2个数据框：

ID             word
1              srv1
2              srv2
3              srv1
4              nan
5              srv3
6              srv1
7              srv5
8              nan

ID             word
1              nan
2              srv12
3              srv10
4              srv8
5              srv4
6              srv7
7              nan
8              srv9

我需要的是将ID上的那两个数据帧合并，并合并列字以获得：

ID             word
1              srv1 
2              srv2 , srv12
3              srv1 , srv10
4              srv8
5              srv3 , srv4
6              srv1 , srv7
7              srv5
8              srv9

使用以下代码

merge = pandas.merge(df1,df2,on="ID",how="left")
merge["word"] = merge[word_x] + " , " + merge["word_y"]

我得到：

ID             word
1              nan 
2              srv2 , srv12
3              srv1 , srv10
4              nan
5              srv3 , srv4
6              srv1 , srv7
7              nan
8              nan

这不是正确的解决方案。

Answer 1

即使na_rep中的源列之一，也可以使用Series.str.cat和word选项填充nan列，然后使用str.strip修剪单词之间的前导/后缀' , '。

m['word'] = m['word_x'].str.cat(m['word_y'], sep=' , ', na_rep='').str.strip(' , ')

返回

   ID word_x word_y          word
0   1   srv1    NaN          srv1
1   2   srv2  srv12  srv2 , srv12
2   3   srv1  srv10  srv1 , srv10
3   4    NaN   srv8          srv8
4   5   srv3   srv4   srv3 , srv4
5   6   srv1   srv7   srv1 , srv7
6   7   srv5    NaN          srv5
7   8    NaN   srv9          srv9

Answer 2

您可以使用np.select选择现有值或串联值。

尝试一下：

import pandas as pd
import numpy as np
from io import StringIO

df1 = pd.read_csv(StringIO("""
ID             word
1              srv1
2              srv2
3              srv1
4              nan
5              srv3
6              srv1
7              srv5
8              nan"""), sep=r"\s+")

df2 = pd.read_csv(StringIO("""
ID             word
1              nan
2              srv12
3              srv10
4              srv8
5              srv4
6              srv7
7              nan
8              srv9"""), sep=r"\s+")


conditions = [(~df1["word"].isna()) & df2["word"].isna(), df1["word"].isna() & (~df2["word"].isna()), (~df1["word"].isna()) & (~df2["word"].isna())]
choices = [df1["word"], df2["word"], df1["word"] + "," + df2["word"]]

df1["word"] = np.select(conditions,choices)

print(df1)

输出：

   ID        word
0   1        srv1
1   2  srv2,srv12
2   3  srv1,srv10
3   4        srv8
4   5   srv3,srv4
5   6   srv1,srv7
6   7        srv5
7   8        srv9

Answer 3

根据我认为您想做的事，我会首先去除那些nan：

df_1.fillna(value="")
df_2.fillna(value="")

然后我将再次尝试合并，看看您是否得到了想要的东西。