numpy - one array calculates the average based on the position of another array

Question

I have 2 arrays with the same shape. If the value of the element of the bList array corresponding to the aList array is 255, then find the corresponding position in the aList array, and add the eligible elements of the a array to calculate the average.

I think I can do it with loop but I think it's stupid.

import numpy as np

aList = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
bList = np.array([[255,255,0,255], [0,0,255,255], [255,0,0,0]])
sum_list = []
for id,row in enumerate(aList):
    for index,ele in enumerate(row):
        if bList[id][index]==255:
            tmp = aList[id][index]
            sum_list.append(tmp)
average = np.mean(sum_list) #(1+2+4+7+8+9)/6 #5.166666666666667

Is there a simpler way?

Yes, you should make that an answer so it can be rewarded.

Tim Roberts
– Tim Roberts

2022-02-22 00:17:55 +00:00
Commented Feb 22, 2022 at 0:17 — Tim Roberts
– Tim Roberts, Commented Feb 22, 2022 at 0:17

mozway · Accepted Answer · 2022-02-22 01:12:14Z

1

Use numpy.where

np.mean(aList[np.where(bList==255)])

Or with a boolean mask:

mask = (bList==255)

(aList*mask).sum()/mask.sum()

Output: 5.166666666666667

answered Feb 22, 2022 at 1:12

mozway

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