Many times we have non-numeric values in NumPy array. These values need to be removed, so that array will be free from all these unnecessary values and look more decent. It is possible to remove all columns containing Nan values using the Bitwise NOT operator and np.isnan() function.
Example 1:
Python3
import numpy as np
n_arr = np.array([[ 10.5 , 22.5 , np.nan],
[ 41 , 52.5 , np.nan]])
print ( "Given array:" )
print (n_arr)
print ( "\nRemove all columns containing non-numeric elements " )
print (n_arr[:, ~np.isnan(n_arr). any (axis = 0 )])
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Output:

In the above example, we remove columns containing non-numeric values from the 2X3 Numpy array.
Example 2:
Python3
import numpy as np
n_arr = np.array([[ 10.5 , 22.5 , 10.5 ],
[ 41 , 52.5 , 25 ],
[ 100 , np.nan, 41 ]])
print ( "Given array:" )
print (n_arr)
print ( "\nRemove all columns containing non-numeric elements " )
print (n_arr[:, ~np.isnan(n_arr). any (axis = 0 )])
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Output:

In the above example, we remove columns containing non-numeric values from the 3X3 Numpy array.
Example 3:
Python3
import numpy as np
n_arr = np.array([[ 10.5 , 22.5 , 3.8 ],
[ 23.45 , 50 , 78.7 ],
[ 41 , np.nan, np.nan],
[ 20 , 50.20 , np.nan],
[ 18.8 , 50.60 , 8.8 ]])
print ( "Given array:" )
print (n_arr)
print ( "\nRemove all columns containing non-numeric elements " )
print (n_arr[:, ~np.isnan(n_arr). any (axis = 0 )])
|
Output:

In the above example, we remove columns containing non-numeric values from the 5X3 Numpy array.