Python 按元素减去两个数据帧的值

作为对象构造函数的一部分，我想从元素角度减去两个数据帧的值：

        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

及

借

每个元素的结果为NaN，两个dfs的连接为：

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

---

将单个列相互减去没有问题。不幸的是，这不是理想的解决方案，因为我想保持未定义的列数。

如果两个数据帧中相同的列名称和索引减去由第二个

数据帧创建的numpy数组：

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

如果列名称的位置匹配，则使用
重命名
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

确保形状、索引和列相等，然后减法将在元素方面起作用。
self.dfresidualLoad=self.dfsupply - self.dfload.values

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