Python Pandas：在多個條件下合並數據框

Question

我希望合並在多個條件下通過sql獲取的數據幀。

• df1：第一個df包含客戶ID，群集ID和客戶區域ID。
• 第二個df包含投訴ID，注冊號。

df1和df2如下所示：

df1

Customer ID     Cluster ID  Customer Zone ID
CUS1001.A       CUS1001.X   CUS1000
CUS1001.B       CUS1001.X   CUS1000
CUS1001.C       CUS1001.X   CUS1000
CUS1001.D       CUS1001.X   CUS1000
CUS1001.E       CUS1001.X   CUS1000
CUS2001.A       CUS2001.X   CUS2000

df2：

Complain ID RegistrationNumber   Status
CUS3501.A       99231            open
CUS1001.B       21340            open
CUS1001.X       32100            open

我希望在以下條件下合並這兩個數據框：

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.

最終結果應如下所示：

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

請幫忙！

Answer 1

嘗試...使用pandas ： melt ， merge和concat

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         

通過使用numpy的intersect1d ，我個人比以前更喜歡這種方法。

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