How to replace values in a numpy array based on 2 other columns

Question

Let say I have the following array:

import numpy as np

a = np.random.randint(10, size=(3,5))

print(a)

Output looks like below:

[[4 0 3 7 3]
[6 5 7 3 3]
[6 6 4 2 1]]

If I want to select the row where column 0 element is 4 and replace column 2 element with 6, I can do the following.

a[a[:, 0] == 4, 2] = 6

Output will be as below:

[[4 0 6 7 3]
[6 5 7 3 3]
[6 6 4 2 1]]

How can I select the row where column 0 element is 6 AND column 1 element is 5 and replace column 2 element with 9 so that the output is as below:

[[4 0 6 7 3]
[6 5 9 3 3]
[6 6 4 2 1]]

Related:Replacing elements in a numpy array when there are multiple conditions — wwii
– wwii, Commented Nov 3, 2020 at 15:33

mck · Accepted Answer · 2020-11-03 14:17:41Z

4

You can do a bitwise AND on the conditions:

a[(a[:, 0] == 6) & (a[:, 1] == 5), 2] = 9

answered Nov 3, 2020 at 14:17

mck

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Quang Hoang · Accepted Answer · 2020-11-03 14:18:46Z

2

You can compare the array row-wise with a tuple/list and use all:

a[(a[:,:2]==(6,5)).all(axis=1), 2] = 9

answered Nov 3, 2020 at 14:18

Quang Hoang

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