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 rows 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, 3.8], [41, np.nan, np.nan]]) print("Given array:") print(n_arr) print("\nRemove all rows containing non-numeric elements") print(n_arr[~np.isnan(n_arr).any(axis=1)]) |
Output:
In the above example, we remove row 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, 3.8], [23.45, 50, 78.7], [41, np.nan, np.nan]]) print("Given array:") print(n_arr) print("\nRemove all rows containing non-numeric elements") print(n_arr[~np.isnan(n_arr).any(axis=1)]) |
Output:
In the above example, we remove row containing non-numeric values from the 3X3 Numpy array.
Example 3:
Python3
# Importing Numpy module import numpy as np # Creating 5X4 2-D Numpy array n_arr = np.array([[10.5, 22.5, 3.8, 5], [23.45, 50, 78.7, 3.5], [41, np.nan, np.nan, 0], [20, 50.20, np.nan, 2.5], [18.8, 50.60, 8.8, 58.6]]) print("Given array:") print(n_arr) print("\nRemove all rows containing non-numeric elements") print(n_arr[~np.isnan(n_arr).any(axis=1)]) |
Output:
In the above example, we remove rows containing non-numeric values from the 5X4 Numpy array.
