Python Pandas：在多個條件下合併數據幀

Question

我希望在多個條件下合併通過sql獲取的數據幀。

• df1：第一個df包含Customer ID，Cluster ID和Customer Zone ID。
• 第二個df包含投訴ID，註冊號。

的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.X0   open 

我要合併用以下的條件這兩個數據幀：

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. 

最終結果應該是這樣的：

Customer ID Cluster ID Customer Zone ID Complain ID Regi ID Status 
CUS1001.A CUS1001.X  CUS1000   CUS1001.X0 open 
CUS1001.B CUS1001.X  CUS1000   CUS1001.B 21340 open 
CUS1001.C CUS1001.X  CUS1000   CUS1001.X0 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.X0.0 
1 CUS1001.B CUS1001.X   CUS1000 CUS1001.B    21340.0 
2 CUS1001.C CUS1001.X   CUS1000 CUS1001.X0.0 
3 CUS1001.D CUS1001.X   CUS1000 CUS1001.X0.0 
4 CUS1001.E CUS1001.X   CUS1000 CUS1001.X0.0 
5 CUS2001.A CUS2001.X   CUS2000   0     0.0 
    Status  
0 open   
1 open   
2 open   
3 open   
4 open   
5  0   

更新 通過使用numpy的的intersect1d，我個人很喜歡這種方法最爲比前一個。

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 
0.0 open 
1    21340.0 open 
0.0 open 
0.0 open 
0.0 open 
5     NaN NaN