Numpy dot() function is used to perform matrix multiplication.
You might be thinking if you can multiply two arrays then why there is a need for the dot() function. Let's understand why direct multiplication of arrays does not work:
import numpy as np
a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 10], [15, 20]])
c = a*b
print('Matrix a:\n', a)
print('Matrix b:\n', b)
print('Resultant Matrix:\n ', c)
Output
Matrix a: [[1 2] [3 4]] Matrix b: [[ 5 10] [15 20]] Resultant Matrix: [[ 5 20] [45 80]]
Note: Following tutorials are essential to improve your understanding of performing various matrix operations in NumPy:
As you can see direct multiplication of two NumPy arrays does not produce the desired result. It is just multiplying corresponding elements of two arrays. That is why dot() function is introduced.
Let's see the syntax of dot() function:
np.dot(a, b)
Both a and b parameter refer to two-dimensional Numpy array.
Note: Before you perform matrix multiplication, make sure that the number of columns in the first matrix is equal to the number of rows in the second matrix.
#Python program to test np.dot() function
import numpy as np
a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 10], [15, 20]])
c = np.dot(a, b)
print('Matrix a:\n', a)
print('Matrix b:\n', b)
print('Resultant Matrix:\n ', c)
Output
Matrix a: [[1 2] [3 4]] Matrix b: [[ 5 10] [15 20]] Resultant Matrix: [[ 35 50] [ 75 110]]
