使用列表推导 Python 将值添加到数组列表的第一个索引

Question

The Vals list comprehension below modifies Values such that for the number of nth rows it indexes the array values as such. How would I be able to add an increment to the Vals list comprehension where it adds 100 in front of all of the modified lists? I want to only modify the list comprehension function to do that.

import numpy as np 

first_index_val = 100
Values = np.array([[130,123,135.3,139.05,156.08,163.88,173.72],
                  [130,123,135.3,139.05,156.08,163.88,173.72],
                  [130,123,135.3,139.05,156.08,163.88,173.72],
                  [130,123,135.3,139.05,156.08,163.88,173.72],
                  [130,123,135.3,139.05,156.08,163.88,173.72],
                  [130,123,135.3,139.05,156.08,163.88,173.72],
                  [130,123,135.3,139.05,156.08,163.88,173.72]])

Vals = np.array([arr[i:] for i,arr in enumerate(Values.tolist())])

Output:

[list([130.0, 123.0, 135.3, 139.05, 156.08, 163.88, 173.72])
 list([123.0, 135.3, 139.05, 156.08, 163.88, 173.72])
 list([135.3, 139.05, 156.08, 163.88, 173.72])
 list([139.05, 156.08, 163.88, 173.72]) list([156.08, 163.88, 173.72])
 list([163.88, 173.72]) list([173.72])]

Expected Output:

[list([100, 130.0, 123.0, 135.3, 139.05, 156.08, 163.88, 173.72])
 list([100, 123.0, 135.3, 139.05, 156.08, 163.88, 173.72])
 list([100, 135.3, 139.05, 156.08, 163.88, 173.72])
 list([100, 139.05, 156.08, 163.88, 173.72]) list([100, 156.08, 163.88, 173.72])
 list([100, 163.88, 173.72]) list([100, 173.72])]

Answer 1

Just add in the addition to the list comprehension.

Vals = np.array([100] + [arr[i:] for i,arr in enumerate(Values.tolist())])

Answer 2

Here is my take on it, simple and declarative.

import numpy as np

first_index_val = 100
values = np.array([[130, 123, 135.3, 139.05, 156.08, 163.88, 173.72],
               [130, 123, 135.3, 139.05, 156.08, 163.88, 173.72],
               [130, 123, 135.3, 139.05, 156.08, 163.88, 173.72],
               [130, 123, 135.3, 139.05, 156.08, 163.88, 173.72],
               [130, 123, 135.3, 139.05, 156.08, 163.88, 173.72],
               [130, 123, 135.3, 139.05, 156.08, 163.88, 173.72],
               [130, 123, 135.3, 139.05, 156.08, 163.88, 173.72]])


flipped = np.flip(values, 1)

appended = np.array([np.append(x, first_index_val) for x in flipped])

print(np.flip(appended, 1))