Following is a tutorial on how to square a matrix in Numpy Python library.
The easiest way and the most convenient one is just to use built-in function of Numpy square. It just take my array as an argument and squares it.
import numpy as np
my_array = np.array([1, 2, 3, 4])
squared_array = np.square(my_array)
print(f"My array is equal to {my_array}")
print(f"Squared array is equal to {squared_array}")
As an output you can see that my array got squared as expected.
You can also use power Numpy method. Square is the same as second power so it will work the same.
import numpy as np
my_array = np.array([1, 2, 3, 4])
squared_array = np.power(my_array, 2)
print(f"My array is equal to {my_array}")
print(f"Squared array is equal to {squared_array}")
The most pythonic way would be to use ** 2 which also square my array.
import numpy as np
my_array = np.array([1, 2, 3, 4])
squared_array = my_array ** 2
print(f"My array is equal to {my_array}")
print(f"Squared array is equal to {squared_array}")
These are 3 different ways to square a matrix using Numpy. Of course Numpy square function is the most efficient one for a comercial purpose.