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.

Nevermind, found it:

ar3=ar2[~np.isnan(ar2).any(axis=1)]

Thanks anyway

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 could use boolean indexing to select only those columns which do not contain nan:

>>> x[:, ~np.isnan(x).any(axis=0)]
array([[ 1.,  2.,  4.],
       [ 2.,  4.,  8.],
       [ 3.,  6.,  0.]])

(This is nearly identical to the answer you've linked to; only the axes have been switched.)