How to use an isinstance qualifier on the elements of a numpy array

Question

I can do

arr = np.random.randint(0, 1, size=(10,10))
arr == 1

and get a boolean array as an output.

What if my array is an object data type and I want to check that certain elements are an instance of some class? Is there a native way to do it?

The first line makes a (10,10) array of int dtype and all values 0. The second line replaces it with a scalar 1. I don't see where you get an boolean array. Nor do I see object dtype array. — hpaulj
– hpaulj, Commented Mar 6, 2021 at 17:23
For an object dtype array, you check the isinstance of the objects the same way as you would in a list.. Other than allowing things like reshape, object dtype array adds nothing to lists. — hpaulj
– hpaulj, Commented Mar 6, 2021 at 17:41
Oops, I see that you used == not =. I wish people would show the output of their code. I do that all time in my answers. But I don't see the relevance of that to the isinstance question. The boolean aray has a bool dtype. Checking the array dtype is different from checking element class. — hpaulj
– hpaulj, Commented Mar 7, 2021 at 3:20

Tom · Accepted Answer · 2021-03-06 17:47:03Z

2

Looks like numpy.vectorize is an option that numpy provides for doing so:

>>> np_isinstance = np.vectorize(isinstance)
>>> np_isinstance(arr, str)

array([[False, False, False, False, False, False, False, False, False,
        False],
       [False, False, False, False, False, False, False, False, False,
        False],
       [False, False, False, False, False, False, False, False, False,
        False],
       [False, False, False, False, False, False, False, False, False,
        False],
       [False, False, False, False, False, False, False, False, False,
        False],
       [False, False, False, False, False, False, False, False, False,
        False],
       [False, False, False, False, False, False, False, False, False,
        False],
       [False, False, False, False, False, False, False, False, False,
        False],
       [False, False, False, False, False, False, False, False, False,
        False],
       [False, False, False, False, False, False, False, False, False,
        False]])

But see this post about efficiency; it is basically doing a for loop, so there aren't the same efficiency benefits of built-in numpy methods. Other options are also discussed on the thread, if you are interested.

edited Mar 6, 2021 at 17:47

answered Mar 6, 2021 at 16:28

Tom

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1 Comment

Alexander Soare Over a year ago

Thanks for that and the heads up. Will leave it open for a bit before marking as answered

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