Pandas逐個元素地減去兩個數據幀的值

Question

作為對象構造函數的一部分，我想逐個元素地減去兩個pandas數據框的值：

        self.dfload=pd.read_csv(self.name +'/' + 'load.csv')                    
        self.dfload.set_index('snapshot', inplace=True)

                         load_1      load_2      load_3      load_4  
snapshot                                                              
2018-01-01 00:00:00   68.248569   91.998188   64.988923  139.535086   
2018-01-01 00:15:00  138.274243  127.186259   80.769227   33.509007   
2018-01-01 00:30:00  129.824298   56.706114   75.234845  138.610287   
2018-01-01 00:45:00   51.754610   45.703056   73.060490   36.913774   
2018-01-01 01:00:00   52.129775  139.315283   67.093788   60.488806

和

        self.dfsupply=pd.read_csv(self.name + '/' + 'supply.csv')               
        self.dfsupply.set_index('snapshot', inplace=True)

                      supply_1   supply_2   supply_3   supply_4     
snapshot                                                                     
2018-01-01 00:00:00  28.448017  45.383377  56.626144  40.044848    
2018-01-01 00:15:00  37.534878  29.094980  67.722537  15.002448    
2018-01-01 00:30:00  46.163805  28.324557  26.322953  23.250904     
2018-01-01 00:45:00  48.192774  55.049855  72.872200  21.602035     
2018-01-01 01:00:00  60.499436  53.698329  74.674572  42.425620

通過

self.dfresidualLoad=self.dfsupply.subtract(self.dfload, axis='column')

結果是每個元素的NaN和兩個dfs的串聯：

                         load_1  load_2  load_3  ...  supply_1  supply_2 ...
snapshot                                                                        
2018-01-01 00:00:00     NaN     NaN     NaN     NaN     NaN     NaN
.
.         

通過相互減去單個列沒有問題。 不幸的是，這不是理想的解決方案，因為我想保持列數不確定。

Answer 1

如果兩個DataFrame中的相同列名稱和索引減去由第二個DataFrame創建的numpy數組：

self.dfresidualLoad=self.dfsupply - self.dfload.values

或者，如果列名稱的位置匹配，則使用rename列：

d = dict(zip(dfload.columns, dfsupply.columns))
df = dfsupply.subtract(dfload.rename(columns=d), axis='column')
print (df)
                       supply_1   supply_2   supply_3    supply_4
2018-01-01 00:00:00  -39.800552 -46.614811  -8.362779  -99.490238
2018-01-01 00:15:00 -100.739365 -98.091279 -13.046690  -18.506559
2018-01-01 00:30:00  -83.660493 -28.381557 -48.911892 -115.359383
2018-01-01 00:45:00   -3.561836   9.346799  -0.188290  -15.311739
2018-01-01 01:00:00    8.369661 -85.616954   7.580784  -18.063186