使用 Numpy Python 提高迭代 function 的性能

Question

Is there a way I could run the function below on pure numpy, so I am trying to get rid of the for loop. I am trying to decrease the runtime of my code. Number_array is added on with each each element of Values and summed. The sum is appended onto the Appending_list after each iteration. The Number_array makes 10000000 integers between 0 to 100.

Code:

import numpy as np
from numpy import random
Appending_list = []
Values = random.randint(100, size=(100000))
Number_array = random.randint(100, size=(1000))
for n in range(len(Values)):
    result = np.sum([Number_array + Values[n]])
    Appending_list.append(result)

Performance:

Answer 1

Your code for comparison:

Appending_list = []
for n in range(len(Values)):
    result = np.sum([Number_array + Values[n]])
    Appending_list.append(result)

Writing the sum a different way:

Appending_list = []
for n in range(len(Values)):
    result = np.sum(Number_array) + Values[n] * len(Number_array)
    Appending_list.append(result)

And that is easy to vectorize:

Appending_list = np.sum(Number_array) + Values * len(Number_array)