用for循环附加列表并在python中的每个单元格中获取多个值

Question

我正在尝试使用 for 循环运行 3 次优化并将结果存储在一个数据帧中。

在每次优化（for 循环的元素）之后，我会附加结果列表并能够在一个列表中获得所有结果。但是，当我尝试将列表转换为数据框时，每个优化都得到一行，每个单元格中对应于变量名称和优化编号的多个值如下所示：

Date = []
results = []


for idx, df in enumerate([df0,df1,df2]):

    model = ConcreteModel()
    
    model.T = Set(initialize=df.hour.tolist(), ordered=True)

...


    # Solve model
    solver = SolverFactory('glpk')
    solver.solve(model) 
    
    Date = list(df['Date'])
    results.append([Date, model.Ein.get_values().values(), model.Eout.get_values().values(), 
                    model.Z.get_values().values(), model.NES.get_values().values(), 
                    model.L.get_values().values()])
    
df_results = pd.DataFrame(results)
df_results.rename(columns = {0: 'Date', 1: 'Ein', 2:'Eout', 3:'Z', 4:'NES', 5:'L'}, inplace = True)
df_results

## The output of the df is:

                                              Date                                              Ein                         
0   [2019-01-01, 2019-01-01, 2019-01-01, 2019-01-0...   (0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, ...   (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   (0.0, 0.0, 1.0, 2.0, 3.0, 3.0, 4.0, 5.0, 5.0, ...   (0.0, 0.0, -100.0, -100.0, -100.0, 0.0, -100.0...   (16231.0, 16051.0, 15806.0, 15581.0, 15610.0, ...
1   [2019-01-16, 2019-01-16, 2019-01-16, 2019-01-1...   (0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, ...   (0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.0, 0.0, 0.0, ...   (0.0, 1.0, 1.0, 1.0, 1.0, 0.5, 1.5, 2.5, 3.5, ...   (0.0, -100.0, 0.0, 0.0, 0.0, 50.0, -100.0, -10...   (17643.0, 18654.0, 20462.0, 20448.0, 20305.0, ...
2   [2019-01-31, 2019-01-31, 2019-01-31, 2019-01-3...   (0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...   (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, ...   (0.0, 0.0, 1.0, 1.0, 2.0, 3.0, 4.0, 3.0, 3.0, ...   (0.0, 0.0, -100.0, 0.0, -100.0, -100.0, -100.0...   (22155.0, 22184.0, 21510.0, 21193.0, 20884.0, ...


#The output of the list named results is:

[[['2019-01-01',
   '2019-01-01',
   '2019-01-01',
...
   '2019-01-15',
   '2019-01-15',
   '2019-01-15',
   '2019-01-15',
   '2019-01-15',
   '2019-01-15',
   '2019-01-16',
   '2019-01-16',
   '2019-01-16',
   '2019-01-16',
   '2019-01-16'],
  dict_values([0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
...
-1.11022302462516e-16, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.11022302462516e-16, 0.0, 1.0, 0.5, 0.0, 0.0, 0.0, 0.166666666666667, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.333333333333333, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.666666666666667, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
  dict_values([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.5, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.25, 
...
0.333333333333333, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.166666666666667, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.833333333333333, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.166666666666667, 0.0, 0.0, 0.0, 0.666666666666667, 0.333333333333333, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.5, 0.25, 0.125, 0.0625, 0.03125, 0.015625, 0.0078125, 0.00390625, 0.001953125, 0.0009765625, 0.00048828125, 0.0]),
  dict_values([0.0, 0.0, 1.0, 2.0, 3.0, 3.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 4.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 0.5, 0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 4.5, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 3.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 4.0, 5.0, 5.0, 5.0, 4.0, 3.0, 3.0, 3.0, 
...
0.142857142857143, 0.142857142857143, 1.0, 2.0, 3.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 3.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 4.0, 5.0, 5.0, 5.0, 4.0, 3.0, 3.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.33333333333333, 1.33333333333333, 0.666666666666667, 0.666666666666667, 0.5, 0.25, 0.125, 0.0625, 0.03125, 0.015625, 0.0078125, 0.00390625, 0.001953125, 0.0009765625, 0.00048828125, 0.00048828125]),
 [['2019-01-16',
   '2019-01-16',
   '2019-01-16',
   '2019-01-16',
   '2019-01-16',
   '2019-01-16',
   '2019-01-16',
   '2019-01-16',
   '2019-01-16',
   '2019-01-16',
   '2019-01-16',
   '2019-01-16',
   '2019-01-16',
   '2019-01-16',
   '2019-01-16',
...


Is it because each result in the for loop has de different dictionary? How could my results in this form:

       Date       Ein   Eout   Z   NES        L                                                     
0   2019-01-01    1.0   0.0   1.0 -100.0  16231.0
1   2019-01-01    1.0   1.0   0.0  100.0  16051.0,  
...

Answer 1

您不断地附加到results，创建一个错误维度的列表列表。我希望这个解决方案对你有用 -

df_results  = pd.DataFrame(zip(Date, model.Ein.get_values().values(), model.Eout.get_values().values(), 
                    model.Z.get_values().values(), model.NES.get_values().values(), 
                    model.L.get_values().values()))

如果没有，请告诉我。