To remove NaN values from a NumPy array x:

x = x[~numpy.isnan(x)]

Explanation

The inner function numpy.isnan returns a boolean/logical array which has the value True everywhere that x is not-a-number. Since we want the opposite, we use the logical-not operator ~ to get an array with Trues everywhere that x is a valid number.

Lastly, we use this logical array to index into the original array x, in order to retrieve just the non-NaN values.

The question has changed, so too has the answer:

Strings can't be tested using math.isnan as this expects a float argument. In your countries list, you have floats and strings.

In your case the following should suffice:

cleanedList = [x for x in countries if str(x) != 'nan']

Old answer

In your countries list, the literal 'nan' is a string not the Python float nan which is equivalent to:

float('NaN')

In your case the following should suffice:

cleanedList = [x for x in countries if x != 'nan']

This is probably because the np.isnan() is failing to deal with string types among your possible element types in collection. You could try to use panda's isnull() to remove NaN values.

import pandas as pa
import numpy as np

a = ['A', np.nan, np.nan, 1.67, 8]
a = [x for x in a if not pa.isnull(x)]
print(a)

You can just replace NaN and infinite values with the following mask:

output[~np.isfinite(output)] = 0

>>> output
array([[1.        , 0.5       , 1.        , 1.        , 0.        ,
        1.        ],
       [1.        , 1.        , 0.5       , 1.        , 0.46524064,
        1.        ],
       [1.        , 1.        , 1.        , 0.        , 1.        ,
        1.        ]])