It is impossible to carry out certain arithmetic operations if two arrays have different shapes. In this situation, Python provides you with a built-in `numpy.array()` broadcasting feature that broadcasts the smaller array across the larger array. 
Take a look at the example below:   

# Solution
As stated earlier, we will `broadcast` the smaller array across the larger array to perform this arithmetic operation.

# Possible cases
One of the possible cases (shown above) that makes broadcasting possible is when two matrices have the same columns. However​, we have two more cases:



# importing numpy library
import numpy as np 
# MatA is a 3x3 matrix
MatA = np.array([[1,2,3],[4,5,6],[7,8,9]])
# MatB is also a 1x3 matrix 
MatB = np.array([[1],[2],[3]])
# Arithmetic operation is possible because 
# of broadcasting
print (MatA + MatB)

SameColumns

# importing numpy library
import numpy as np 
# MatA is a 3x3 matrix
MatA = np.array([[1,2,3],[4,5,6],[7,8,9]])
# MatB is also a 3x1 matrix 
MatB = np.array([1,2,3])
# Arithmetic operation is possible because 
# of broadcasting
print (MatA + MatB)

SameRows

# importing numpy library
import numpy as np 
# MatA is a 3x3 matrix
MatA = np.array([[1,2,3],[4,5,6],[7,8,9]])
# MatB is also a 1x1 matrix 
MatB = np.array([1])
# Arithmetic operation is possible because 
# of broadcasting
print (MatA + MatB)

What is broadcasting in Numpy? 

Broadcasting enables arithmetic operations on arrays of different shapes by extending the smaller array across the larger one.

What is broadcasting in Numpy?

Solution

Possible cases

Code