CODEWITHSUNDEEP
pythonnumpynumpy-ndarray
Kajal_T
Kajal_T

Reputation: 73

Why changes in Numpy array are not reflecting in two different cases?

I am confused about below two codes:

1st code: Changes getting reflected in both array

    import numpy as nm

    ab=nm.arange(10)
    ba=ab
    ba[0]=99
    print(ba)
    print (ab)

Output:

ba=[99  1  2  3  4  5  6  7  8  9]

ab=[99  1  2  3  4  5  6  7  8  9]

2nd code: Changes NOT getting reflected in both array

    import numpy as nm

    ab=nm.arange(10)
    ba=ab
    ba=ab-ab
    print(ba)
    print(ab)

Output:

ba=[0 0 0 0 0 0 0 0 0 0]

ab=[0 1 2 3 4 5 6 7 8 9]

Can anybody please explain this? I want to understand why is it happening? I can see new address is allocated in 2nd case but why is not overwriting the data like in 1st case?

Upvotes: 0

Views: 133

Answers (1)

vlizana
vlizana

Reputation: 3232

The variable that holds the array actually holds the memory address where the array is located, by doing ba=ab you're setting the same address for both arrays, so if you change one of them the changes will be reflected in the other, but by doing ba=ab-ab you're overwriting this address with the result of an evaluation, and as it is new data it has to be stored in a new memory address.

Upvotes: 2

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