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
# Importing Numpy module import numpy as np # Creating 2X3 2-D Numpy array 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)]) |
Output:
In the above example, we remove columns containing non-numeric values from the 2X3 Numpy array.
Example 2:
Python3
# Importing Numpy module import numpy as np # Creating 3X3 2-D Numpy array 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)]) |
Output:
In the above example, we remove columns containing non-numeric values from the 3X3 Numpy array.
Example 3:
Python3
# Importing Numpy module import numpy as np # Creating 5X3 2-D Numpy array 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.
