Broadcasting arrays

Python lists do not support direct vectorizing arithmetic operations. NumPy offers a faster-vectorized array operation compared to Python list loop-based operations. Here, all the looping operations are performed in C instead of Python, which makes it faster. Broadcasting functionality checks a set of rules for applying binary functions, such as addition, subtraction, and multiplication, on different shapes of an array.

Let's see an example of broadcasting:

# Create NumPy Array
arr1 = np.arange(1,5).reshape(2,2) 
print(arr1)

Output:
[[1 2]
 [3 4]]

# Create another NumPy Array
arr2 = np.arange(5,9).reshape(2,2) 
print(arr2)

Output:
[[5 6]
 [7 8]]

# Add two matrices
print(arr1+arr2)

Output:
[[ 6 8]
 [10 12]]

In all three preceding examples, we can see the addition of two arrays of the same size. This concept is known as broadcasting:

# Multiply two matrices
print(arr1*arr2)

Output:
[[ 5 12]
 [21 32]]

In the preceding example, two matrices were multiplied. Let's perform addition...