Python：在多个条件下合并数据帧

我希望在多种条件下合并通过sql获取的数据帧

df1：第一个df包含客户ID、集群ID和客户区域ID。 第二个df包含投诉ID、注册号。 df1和df2如下所示：

df1

df2：

我希望在以下条件下合并这两个数据帧：

if(Complain ID == Customer ID):
    Merge on Customer ID
Elif(Complain ID == Cluster ID):
    Merge on Customer ID
Elif (Complain ID == Customer Zone ID):
    Merge on Customer ID
Else:
    Merge empty row.

最终结果应如下所示：

Customer ID Cluster ID  Customer Zone ID   Complain ID  Regi ID  Status
CUS1001.A   CUS1001.X       CUS1000         CUS1001.X    32100    open
CUS1001.B   CUS1001.X       CUS1000         CUS1001.B    21340    open
CUS1001.C   CUS1001.X       CUS1000         CUS1001.X    32100    open
  .             .               .               .           .       .
  .             .               .               .           .       .
CUS2001.A   CUS2001.X       CUS2000             0           0       0

请帮忙

试试这个……使用熊猫：融化、合并和浓缩

更新 通过使用numpy的intersect1d，我个人最喜欢这种方法，而不是以前的方法

df1.MatchId=[np.intersect1d(x,df2.ComplainID.values) for x in df1[['CustomerID','ClusterID']].values]
df1.MatchId=df1.MatchId.apply(pd.Series)
df1
Out[307]:
  CustomerID  ClusterID CustomerZoneID    MatchId
0  CUS1001.A  CUS1001.X        CUS1000  CUS1001.X
1  CUS1001.B  CUS1001.X        CUS1000  CUS1001.B
2  CUS1001.C  CUS1001.X        CUS1000  CUS1001.X
3  CUS1001.D  CUS1001.X        CUS1000  CUS1001.X
4  CUS1001.E  CUS1001.X        CUS1000  CUS1001.X
5  CUS2001.A  CUS2001.X        CUS2000        NaN

df1.merge(df2,left_on='MatchId',right_on='ComplainID',how='left')
Out[311]: 
  CustomerID  ClusterID CustomerZoneID    MatchId ComplainID  \
0  CUS1001.A  CUS1001.X        CUS1000  CUS1001.X  CUS1001.X   
1  CUS1001.B  CUS1001.X        CUS1000  CUS1001.B  CUS1001.B   
2  CUS1001.C  CUS1001.X        CUS1000  CUS1001.X  CUS1001.X   
3  CUS1001.D  CUS1001.X        CUS1000  CUS1001.X  CUS1001.X   
4  CUS1001.E  CUS1001.X        CUS1000  CUS1001.X  CUS1001.X   
5  CUS2001.A  CUS2001.X        CUS2000        NaN        NaN   
   RegistrationNumber Status  
0             32100.0   open  
1             21340.0   open  
2             32100.0   open  
3             32100.0   open  
4             32100.0   open  
5                 NaN    NaN  

Customer ID Cluster ID  Customer Zone ID   Complain ID  Regi ID  Status
CUS1001.A   CUS1001.X       CUS1000         CUS1001.X    32100    open
CUS1001.B   CUS1001.X       CUS1000         CUS1001.B    21340    open
CUS1001.C   CUS1001.X       CUS1000         CUS1001.X    32100    open
  .             .               .               .           .       .
  .             .               .               .           .       .
CUS2001.A   CUS2001.X       CUS2000             0           0       0

df=pd.melt(df1)
df=df.merge(df2,left_on='value',right_on='Complain ID',how='left')
df['number']=df.groupby('variable').cumcount()
df=df.groupby('number').bfill()
Target=pd.concat([df1,df.iloc[:5,2:6]],axis=1).fillna(0).drop('number',axis=1)

Target
Out[39]: 
  Customer ID Cluster ID Customer Zone ID Complain ID  RegistrationNumber  \
0   CUS1001.A  CUS1001.X          CUS1000   CUS1001.X             32100.0   
1   CUS1001.B  CUS1001.X          CUS1000   CUS1001.B             21340.0   
2   CUS1001.C  CUS1001.X          CUS1000   CUS1001.X             32100.0   
3   CUS1001.D  CUS1001.X          CUS1000   CUS1001.X             32100.0   
4   CUS1001.E  CUS1001.X          CUS1000   CUS1001.X             32100.0   
5   CUS2001.A  CUS2001.X          CUS2000           0                 0.0   
  Status    
0   open         
1   open         
2   open         
3   open         
4   open        
5      0         

df1.MatchId=[np.intersect1d(x,df2.ComplainID.values) for x in df1[['CustomerID','ClusterID']].values]
df1.MatchId=df1.MatchId.apply(pd.Series)
df1
Out[307]:
  CustomerID  ClusterID CustomerZoneID    MatchId
0  CUS1001.A  CUS1001.X        CUS1000  CUS1001.X
1  CUS1001.B  CUS1001.X        CUS1000  CUS1001.B
2  CUS1001.C  CUS1001.X        CUS1000  CUS1001.X
3  CUS1001.D  CUS1001.X        CUS1000  CUS1001.X
4  CUS1001.E  CUS1001.X        CUS1000  CUS1001.X
5  CUS2001.A  CUS2001.X        CUS2000        NaN

df1.merge(df2,left_on='MatchId',right_on='ComplainID',how='left')
Out[311]: 
  CustomerID  ClusterID CustomerZoneID    MatchId ComplainID  \
0  CUS1001.A  CUS1001.X        CUS1000  CUS1001.X  CUS1001.X   
1  CUS1001.B  CUS1001.X        CUS1000  CUS1001.B  CUS1001.B   
2  CUS1001.C  CUS1001.X        CUS1000  CUS1001.X  CUS1001.X   
3  CUS1001.D  CUS1001.X        CUS1000  CUS1001.X  CUS1001.X   
4  CUS1001.E  CUS1001.X        CUS1000  CUS1001.X  CUS1001.X   
5  CUS2001.A  CUS2001.X        CUS2000        NaN        NaN   
   RegistrationNumber Status  
0             32100.0   open  
1             21340.0   open  
2             32100.0   open  
3             32100.0   open  
4             32100.0   open  
5                 NaN    NaN