check if any of the values is `None` in `numpy.array`

Question

I have a 2D numpy.array, which should act as indices for another, so it should contain only ints or bools. Is there a quick and elegant (one line) way for checking for this condition?

I've tried the following code, but it does not provide the desired output, as it only checks for 0s and not for Nones, which is the value present in my case:

Example 1:

a = np.array([[1, 2, 3], [3, 4, 5]])
np.all(type(a) == np.int64, axis=0)

Current Output 1:

False

Example 2:

b = np.array([[1, 2, None], [3, 4, 5]])
np.all(type(a) == np.int64, axis=0)

Current Output 2:

False

so in the first example I'd like to get True as an output, as all the values of this array are numeric, while in example 2 - I'd like to get false, as there is a None value present in the first row of b array. Appreciate any help.

swag2198 · Accepted Answer · 2021-05-13 11:01:31Z

2

This should work:

(b == None).any()

Returns true if any element of b is None.

Note that type(a) will return <class 'numpy.ndarray'> for any numpy array a. That is why your check always returns False irrespective of the presence of None.

You should check a.dtype for the getting the data type. That being said, any array that has None or strings in it will be of type object in numpy.

Example:

import numpy as np

a = np.array([[1, 2, 3], [4, 5, 6]])
a.dtype
# dtype('int64')
type(a)
# <class 'numpy.ndarray'>
(a == None).any()
# False

b = np.array([[1, 2, None], [3, 4, 5]])
b.dtype
# dtype('O')
type(b)
# <class 'numpy.ndarray'>
(b == None).any()
# True
(b == None).any(axis = 0)
# array([False, False,  True])
(b == None).any(axis = 1)
# array([ True, False])

edited May 13, 2021 at 11:01

answered May 13, 2021 at 10:54

swag2198

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