Python 在三列上汇总，在不同条件下汇总，然后合并_Python_Pandas_Dataframe_Pandas Groupby

Python 在三列上汇总，在不同条件下汇总，然后合并

python pandas dataframe

Python 在三列上汇总，在不同条件下汇总，然后合并,python,pandas,dataframe,pandas-groupby,Python,Pandas,Dataframe,Pandas Groupby,我的测试数据帧： print("Create List", end='\n') Test_Data = [('display_name', ['A', 'B', 'B','C','C','C','C','C',]), ('security_type1', ['GOVT', 'CORP','CORP','CORP','CORP','CORP','CORP','CORP']), ('currency_str', ['USD', 'NZD','USD','EUR

我的测试数据帧：

print("Create List",  end='\n') 
Test_Data = [('display_name', ['A', 'B', 'B','C','C','C','C','C',]),
         ('security_type1', ['GOVT', 'CORP','CORP','CORP','CORP','CORP','CORP','CORP']),
         ('currency_str', ['USD', 'NZD','USD','EUR','EUR','GBP','GBP','USD']),
         ('state', ['Done','Passed','Done','Done','Traded Away','Done','Done','Done']),
         ('rfq_qty_CAD_Equiv', [100000, 100000, 100000,100000,100000,100000,100000,100000]),
         ]
dfTest_Data = pd.DataFrame.from_items(Test_Data)
display(dfTest_Data)

display_name    security_type1  currency_str    state   rfq_qty_CAD_Equiv
A                     GOVT          USD          Done         100000
B                     CORP          NZD          Passed       100000
B                     CORP          USD          Done         100000
C                     CORP          EUR          Done         100000
C                     CORP          EUR          Traded Away  100000
C                     CORP          GBP          Done         100000
C                     CORP          GBP          Done         100000
C                     CORP          USD          Done         100000

我想要的结果如下。这里的驱动程序在

display\u name

、

security\u type1

和

currency\u str

上分组<代码>总RFQ和

总RFQ

与

显示名称

display_name    security_type1  currency_str    Done_RFQ    Not_Done_RFQ    Total_RFQ
      A               GOVT           USD            1             0          1  
      B               CORP           USD            1             1          2
      C               CORP           EUR            1             1          5
      C               CORP           GBP            2             0          5
      C               CORP           USD            1             0          5

Hit_Rate    Done_RFQ_Volume     Not_Done_RFQ_Volume     Total_RFQ_Volume
1.00             100000                 0                    100000              
0.50             100000               100000                 200000 
0.20             100000               100000                 500000 
0.40             200000                 0                    500000 
0.20             100000                 0                    500000 


Volume_per_Done_RFQ           Volume_per_Not_Done_RFQ   Volume_per_Total_RFQ
    100000                              0                  100000
    100000                           100000                100000
    100000                           100000                100000
    100000                              0                  100000
    100000                              0                  100000

命中率

完成\u询价

总询价

Volume\u per\u Done\u RFQ

Done\u RFQ\u Volume

Done\u RFQ

Volume\u per\u Not\u Done\u RFQ

Not\u Done\u RFQ\u Volume

Not\u Done\u RFQ

Volume\u per\u Total\u RFQ

Total\u RFQ\u Volume

Total\u RFQ

大部分工作已经完成，我只是在合并第三个数据帧和显示未完成行项目时遇到了问题，其中需要零个条目

print("All Trades",  end='\n') 
d = [
     ('Total_RFQ_Volume', 'sum'), 
     ('Total_RFQ', 'size'), 
    ]
df1 = dfTest_Data.groupby(['display_name'])['rfq_qty_CAD_Equiv'].agg(d)
display (df1)

print("Done Trades",  end='\n') 
d = [
     ('Done_RFQ_Volume', 'sum'), 
     ('Done_RFQ', 'size'), 
    ]
mask = dfTest_Data['state'].str.contains('Done')
df2 = dfTest_Data[mask].groupby(['display_name','security_type1','currency_str'])['rfq_qty_CAD_Equiv'].agg(d).reset_index()
display (df2)

print("Not Done Trades",  end='\n') 
d = [ 
    ('Not_Done_RFQ_Volume', 'sum'), 
    ('Not_Done_RFQ', 'size'), 
] 
mask = ~dfTest_Data['state'].str.contains('Done') 
df3 = dfTest_Data[mask].groupby(['display_name','security_type1','currency_str'])['rfq_qty_CAD_Equiv'] .agg(d) .reset_index()
display (df3)

print("Join Done trades on All Trades",  end='\n') 
df_Done_Client_Hit_Rate_Volume = df2.join(df1, on='display_name').join(df3, on='display_name')
# Create additional calculated columns
df_Done_Client_Hit_Rate_Volume['Hit_Rate'] = df_Done_Client_Hit_Rate_Volume['Done_RFQ'] / df_Done_Client_Hit_Rate_Volume['Total_RFQ'] 
df_Done_Client_Hit_Rate_Volume['Volume_per_Done_RFQ'] = df_Done_Client_Hit_Rate_Volume['Done_RFQ_Volume'] / df_Done_Client_Hit_Rate_Volume['Done_RFQ'] 
df_Done_Client_Hit_Rate_Volume['Volume_per_Not_Done_RFQ'] = df_Done_Client_Hit_Rate_Volume['Not_Done_RFQ_Volume'] / df_Done_Client_Hit_Rate_Volume['Not_Done_RFQ'] 
df_Done_Client_Hit_Rate_Volume['Volume_per_Total_RFQ'] = df_Done_Client_Hit_Rate_Volume['Total_RFQ_Volume'] / df_Done_Client_Hit_Rate_Volume['Total_RFQ'] 
# Reorder columns
df_Done_Client_Hit_Rate_Volume = df_Done_Client_Hit_Rate_Volume[['display_name', 
                                                                 'security_type1',
                                                                 'currency_str', 
                                                                 'Done_RFQ',
                                                                 'Not_Done_RFQ',
                                                                 'Total_RFQ',
                                                                 'Hit_Rate',
                                                                 'Done_RFQ_Volume',
                                                                 'Not_Done_RFQ_Volume',
                                                                 'Volume_per_Done_RFQ',
                                                                 'Volume_per_Not_Done_RFQ',
                                                                 'Total_RFQ_Volume'
                                                                 'Volume_per_Total_RFQ'
                                                                ]]
display (df_Done_Client_Hit_Rate_Volume)

我认为需要首先删除

和df3
的.reset\u index
：
d = [
     ('Done_RFQ_Volume', 'sum'), 
     ('Done_RFQ', 'size'), 
    ]
mask = dfTest_Data['state'].str.contains('Done')
df2 = dfTest_Data[mask].groupby(['display_name','security_type1','currency_str'])['rfq_qty_CAD_Equiv'].agg(d)
#print (df2)

print("Not Done Trades",  end='\n') 
d = [ 
    ('Not_Done_RFQ_Volume', 'sum'), 
    ('Not_Done_RFQ', 'size'), 
] 
mask = ~dfTest_Data['state'].str.contains('Done') 
df3 = dfTest_Data[mask].groupby(['display_name','security_type1','currency_str'])['rfq_qty_CAD_Equiv'].agg(d) 

然后通过和将数据帧连接在一起：
df = pd.concat([df2, df3],axis=1).reset_index()
df_Done_Client_Hit_Rate_Volume = df.join(df1, on='display_name')


最后用0
替换缺少的值：
df_Done_Client_Hit_Rate_Volume = df_Done_Client_Hit_Rate_Volume.fillna(0)
print (df_Done_Client_Hit_Rate_Volume)

  display_name security_type1 currency_str  Done_RFQ_Volume  Done_RFQ  \
0            A           GOVT          USD         100000.0       1.0   
1            B           CORP          NZD              0.0       0.0   
2            B           CORP          USD         100000.0       1.0   
3            C           CORP          EUR         100000.0       1.0   
4            C           CORP          GBP         200000.0       2.0   
5            C           CORP          USD         100000.0       1.0   

   Not_Done_RFQ_Volume  Not_Done_RFQ  Total_RFQ_Volume  Total_RFQ  Hit_Rate  \
0                  0.0           0.0            100000          1       1.0   
1             100000.0           1.0            200000          2       0.0   
2                  0.0           0.0            200000          2       0.5   
3             100000.0           1.0            500000          5       0.2   
4                  0.0           0.0            500000          5       0.4   
5                  0.0           0.0            500000          5       0.2   

   Volume_per_Done_RFQ  Volume_per_Not_Done_RFQ  Volume_per_Total_RFQ  
0             100000.0                      0.0              100000.0  
1                  0.0                 100000.0              100000.0  
2             100000.0                      0.0              100000.0  
3             100000.0                 100000.0              100000.0  
4             100000.0                      0.0              100000.0  
5             100000.0                      0.0              100000.0  

我认为需要首先删除和df3
的.reset\u index
：
d = [
     ('Done_RFQ_Volume', 'sum'), 
     ('Done_RFQ', 'size'), 
    ]
mask = dfTest_Data['state'].str.contains('Done')
df2 = dfTest_Data[mask].groupby(['display_name','security_type1','currency_str'])['rfq_qty_CAD_Equiv'].agg(d)
#print (df2)

print("Not Done Trades",  end='\n') 
d = [ 
    ('Not_Done_RFQ_Volume', 'sum'), 
    ('Not_Done_RFQ', 'size'), 
] 
mask = ~dfTest_Data['state'].str.contains('Done') 
df3 = dfTest_Data[mask].groupby(['display_name','security_type1','currency_str'])['rfq_qty_CAD_Equiv'].agg(d) 

然后通过和将数据帧连接在一起：
df = pd.concat([df2, df3],axis=1).reset_index()
df_Done_Client_Hit_Rate_Volume = df.join(df1, on='display_name')


最后用0
替换缺少的值：
df_Done_Client_Hit_Rate_Volume = df_Done_Client_Hit_Rate_Volume.fillna(0)
print (df_Done_Client_Hit_Rate_Volume)

  display_name security_type1 currency_str  Done_RFQ_Volume  Done_RFQ  \
0            A           GOVT          USD         100000.0       1.0   
1            B           CORP          NZD              0.0       0.0   
2            B           CORP          USD         100000.0       1.0   
3            C           CORP          EUR         100000.0       1.0   
4            C           CORP          GBP         200000.0       2.0   
5            C           CORP          USD         100000.0       1.0   

   Not_Done_RFQ_Volume  Not_Done_RFQ  Total_RFQ_Volume  Total_RFQ  Hit_Rate  \
0                  0.0           0.0            100000          1       1.0   
1             100000.0           1.0            200000          2       0.0   
2                  0.0           0.0            200000          2       0.5   
3             100000.0           1.0            500000          5       0.2   
4                  0.0           0.0            500000          5       0.4   
5                  0.0           0.0            500000          5       0.2   

   Volume_per_Done_RFQ  Volume_per_Not_Done_RFQ  Volume_per_Total_RFQ  
0             100000.0                      0.0              100000.0  
1                  0.0                 100000.0              100000.0  
2             100000.0                      0.0              100000.0  
3             100000.0                 100000.0              100000.0  
4             100000.0                      0.0              100000.0  
5             100000.0                      0.0              100000.0  

它在耶斯雷尔非常有效。我想弄明白这件事，差点把垫圈炸了。谢谢！！它在耶斯雷尔非常有效。我想弄明白这件事，差点把垫圈炸了。谢谢！！