Arrays 在运行linalg.lstsq函数后，我得到了2个numpy数组解决方案，如何将第一个数组列表仅转换为pandas dataframe？

这是输入

S = np_array_x10
T = np_array_y10
X10 = np.linalg.lstsq(S,T)

print(X10)

运行代码后，我得到了两个数组列表解决方案

(array([5140.25083714, 5125.96205785,    0.        , 5247.25816042,
       4340.21555923, 4500.72295881, 3489.78840524, 3975.21412951,
       5091.13006422, 5544.70302696, 5331.61930777, 5175.79643742,
       4313.14110232, 4801.6198475 , 4920.50453911, 4524.01747573,
       7599.72745206, 5250.13341682, 2627.5640602 , 4930.1605991 ,
       5312.62124207]), array([], dtype=float64), 20, array([1.52885657e+04, 7.13804096e+03, 5.99898772e+03, 4.41180973e+03,
       3.84335727e+03, 3.25116063e+03, 2.62341839e+03, 2.50056638e+03,
       2.08240188e+03, 1.62893912e+03, 1.52650905e+03, 1.25984780e+03,
       9.68703407e+02, 6.70143262e+02, 5.69601345e+02, 4.56295033e+02,
       2.84472856e+02, 1.28547885e+02, 1.02008705e+02, 5.59816018e+01,
       1.15798514e-13]))

如何在不将特定数组列表复制到新表达式的情况下，将第一个、第二个和所有数组列表转换为数据帧？

X10 = [5140.25083714, 5125.96205785,    0       , 5247.25816042,
       4340.21555923, 4500.72295881, 3489.78840524, 3975.21412951,
       5091.13006422, 5544.70302696, 5331.61930777, 5175.79643742,
       4313.14110232, 4801.6198475 , 4920.50453911, 4524.01747573,
       7599.72745206, 5250.13341682, 2627.5640602 , 4930.1605991 ,
       5312.62124207]

Final = pd.DataFrame(data = X10, columns= ["A" ,"B" ,   "C" , "D" , "E" , "F",  "G" ,   "H" , "I" , "J" , "K" , "L" , "M" , "N" , "O" , "P" , "Q" , "R" , "S" , "T" , "U"])
Final