在不同的列名称上合并两个不同的数据框 [英] Merge two different dataframes on different column names

问题描述

我有两个数据框，

df1 = pd.DataFrame({'A': ['A1', 'A1', 'A2', 'A3'],
                     'B': ['121', '345', '123', '146'],
                     'C': ['K0', 'K1', 'K0', 'K1']})

df2 = pd.DataFrame({'A': ['A1', 'A3'],
                      'BB': ['B0', 'B3'],
                      'CC': ['121', '345'],
                      'DD': ['D0', 'D1']})

现在我需要从df1的A列和B列以及从df2的A列和CC列获得相似的行. 因此，我尝试了可能的合并选项，例如:

Now I need to get the similiar rows from column A and B from df1 and column A and CC from df2. And so I tried possible merge options, such as:

both_DFS=pd.merge(df1,df2, how='left',left_on=['A','B'],right_on=['A','CC'])

，这将不会为我提供来自df2数据帧的行信息.意思是，我拥有df2中的所有列名，但行只是空或Nan.

and this will not give me row information from df2 dataframe which is what I needed. Meaning, I have all column names from df2 but the rows are just empty or Nan.

然后我尝试:

Both_DFs=pd.merge(df1,df2, how='left',left_on=['A','B'],right_on=['A','CC'])[['A','B','CC']]

这给了我错误，

KeyError: "['B'] not in index"

我的目标是合并具有df1和df2中所有列的Dataframe.任何建议都很好

I am aiming to have a merged Dataframe with all columns from both df1 and df2. Any suggestions would be great

所需的输出:

 Both_DFs
    A   B   C   BB  CC  DD
0   A1  121 K0  B0  121 D0

因此，在我的数据帧(df1和df2)中，只有一行与目标两列都完全匹配.也就是说，df1中的A和B列只有一行与df2中A和CC列中的行完全匹配

So in my data frames (df1 and df2), only one row has exact match for both columns of interest. That is, Column A and B from df1 has only one row matching exactly to rows in columns A and CC in df2

推荐答案

好吧，如果您将列A声明为索引，它将起作用:

Well, if you declare column A as index, it works:

Both_DFs = pd.merge(df1.set_index('A', drop=True),df2.set_index('A', drop=True), how='left',left_on=['B'],right_on=['CC'], left_index=True, right_index=True).dropna().reset_index()

结果是:

    A    B   C  BB   CC  DD
0  A1  123  K0  B0  121  D0
1  A1  345  K1  B0  121  D0
2  A3  146  K1  B3  345  D1

编辑

您只需要:

Both_DFs = pd.merge(df1,df2, how='left',left_on=['A','B'],right_on=['A','CC']).dropna()

哪个给:

    A    B   C  BB   CC  DD
0  A1  121  K0  B0  121  D0

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