Insert into numpy array with condition from another array

Question

I have a numpy array arr containing 0s and 1s,

arr = np.random.randint(2, size=(800,800))

Then I casted it to astype(np.float32) and inserted various float numbers at various positions. In fact, what I would like to do is insert those float numbers only where the original array had 1 rather than 0; where the original array had 0 I want to keep 0.

My thought was to take a copy of the array (with .copy()) and reinsert from that later. So now I have arr above (1s and 0s), and a same-shaped array arr2 with numerical elements. I want to replace the elements in arr2 with those in arr only where (and everywhere where) the element in arr is 0. How can I do this?

Small example:

arr = np.array([1,0],
               [0,1])
arr2 = np.array([2.43, 5.25],
                [1.54, 2.59])

Desired output:

arr2 = np.array([2.43, 0],
                [0, 2.59])

N.B. should be as fast as possible on arrays of around 800x800

Please provide the code that you have worked on.

anisoleanime
– anisoleanime

2021-04-16 14:52:51 +00:00
Commented Apr 16, 2021 at 14:52 — anisoleanime
– anisoleanime, Commented Apr 16, 2021 at 14:52
arr2 * arr ?

Kevin
– Kevin

2021-04-16 15:01:22 +00:00
Commented Apr 16, 2021 at 15:01 — Kevin
– Kevin, Commented Apr 16, 2021 at 15:01

swag2198 · Accepted Answer · 2021-04-16 15:35:22Z

1

Simply do:

arr2[arr == 0] = 0

or

arr2 = arr2 * arr

answered Apr 16, 2021 at 15:35

swag2198

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3 Comments

Vainmonde De Courtenay Over a year ago

Fastest method wins (arr2 = arr2*arr)

Jérôme Richard Over a year ago

Even faster: arr2 *= arr

swag2198 Over a year ago

Nice! Forgot that inplace operations can be used here to further optimize this.

Maxwell Redacted · Accepted Answer · 2021-04-16 15:43:57Z

1

@swag2198 is correct, an alternative is below

Numpy has a functin called 'where' which allows you to set values based on a condition from another array - this is essentially masking

below will achieve what you want - essentially it will return an array the same dimensions as arr2, except wherever there is a zero in arr, it will be replaced with zero

arr = np.array([[1,0],[0,1]])
arr2 = np.array([[2.43, 5.25],
                [1.54, 2.59]])


arr_out = np.where(arr, arr2, 0)

the advantage of this way is that you can pick values based on two arrays if you wish - say you wanted to mask an image for instance - replace the background

answered Apr 16, 2021 at 15:43

Maxwell Redacted

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