Fill numpy array with other numpy array

Question

I have following numpy arrays:

    whole = np.array(
        [1, 0, 3, 0, 6]
    )

    sparse = np.array(
        [9, 8]
    )

Now I want to replace every zero in the whole array in chronological order with the items in the sparse array. In the example my desired array would look like:

    merged = np.array(
        [1, 9, 3, 8, 6]
    )

I could write a small algorithm by myself to fix this but if someone knows a time efficient way to solve this I would be very grateful for you help!

...since you could just write whole[whole==0] = sparse then — FObersteiner
– FObersteiner, Commented Aug 5, 2020 at 18:51
Yes! Awesome, that was exactly what I was looking for. Thank you very much. — Adrian
– Adrian, Commented Aug 5, 2020 at 18:54

Dharman · Accepted Answer · 2020-08-05 19:04:52Z

2

Do you assume that sparse has the same length as there is zeros in whole ?

If so, you can do:

import numpy as np
from copy import copy

whole = np.array([1, 0, 3, 0, 6])
sparse = np.array([9, 8])

merge = copy(whole)
merge[whole == 0] = sparse

if the lengths mismatch, you have to restrict to the correct length using len(...) and slicing.

edited Aug 5, 2020 at 19:04

Dharman♦

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answered Aug 5, 2020 at 18:59

Nathan e

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4 Comments

Nathan e Over a year ago

I was outdated by the comment above, but my method doesn't change 'whole' array in place.

Kevin Over a year ago

Would it be inplace without the copy? I am a little confused about the purpose of the copy.

user3483203 Over a year ago

You should use numpy's copy as opposed to copy.copy

Nathan e Over a year ago

To Kevin : if I don't use copy, when I update 'merge', the original array 'whole' is modified the same way'. So one has to copy the numpy array to keep the original data

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