numpy dot product

In this post, let's learn about the dot product of the Python programming language. Python provides the numpy.dot() function to return the dot product of two arrays.

NumPy is the fundamental package for scientific computing with Python. It is a highly optimised library for numerical operations. The support of NumPy makes the task easier. Several libraries, like OpenCV, SciPy, and Matplotlib, when combined with NumPy, increase the number of weapons in your arsenal.

Syntax of numpy.dot()

Here is the syntax of numpy.dot() method-

numpy.dot(x, y, out=None)

Here, x and y are two input arrays. Both arrays should be 1-D or 2-D. The out is the output argument for 1-D array scalar to be returned. It returns a dot product of two arrays, x and y. If both the arrays 'x' and 'y' are 1-D arrays, the dot() function performs the inner product of vectors and returns a scalar. If both the arrays 'x' and 'y' are 2-D arrays, the dot() function performs the matrix multiplication. If 'x' is an N-dimensional array and 'y' is a 1-dimensional array, then the dot() function performs the sum-product over the last axis of x and y. For N-dimensional arrays, it is a sum product over the last axis of x and the second-last axis of y.

Example1: Numpy Dot product of scalars

In the given example, we have taken two scalars and calculated their dot product using numpy.dot() function. It returns the multiplication of the two scalars.

import numpy as np

a = 30 
b = 24
output = np.dot(a,b)
print(output)

720

Example2: numpy.dot() product of 1D array

In the given example, we have taken two numpy one-dimensional arrays and calculated their dot product using the numpy.dot() function. The output would be an inner product of these two vectors.

import numpy as np

#initialize arrays
A = np.array([5, 2, 9, 3])
B = np.array([6, 9, 1, 7])

#dot product
output = np.dot(A, B)

print(output)

78

Example3: numpy.dot() product of 1D array

Here is another example of numpy dot product of two one-dimensional arrays.

import numpy.matlib 
import numpy as np 

x = np.array([11,23]) 
y = np.array([31,25]) 

print(np.dot(x,y))

916

Example4: numpy.dot() product of 2D array

Here is an example of numpy.dot() of 2-D array. In this, we have taken two two-dimensional arrays and calculated their dot product using numpy.dot() function. The output would be a matrix multiplication of the two input arrays.

import numpy.matlib 
import numpy as np 

x = np.array([[11,23],[2,3]]) 
y = np.array([[31,25],[4,1]]) 

print(np.dot(x,y))

[[433 298]
 [ 74  53]]

Example5: numpy.dot() product of 3D array

Here is an example of numpy.dot() of two 3-D arrays.

import numpy.matlib 
import numpy as np 

x = np.array([[2,4,6],[3,1,4],[8,5,3]]) 
y = np.array([[3,7,2],[2,4,7],[3,5,1]])

print(np.dot(x,y))

[[32 60 38]
 [23 45 17]
 [43 91 54]]

Example6: numpy.dot() product of 1D and 2D arrays

In this, we have taken one 1D and one 2D array and calculated their dot product using the numpy.dot() function.

import numpy.matlib 
import numpy as np 

x = np.array([5, 2])
y = np.array([[6, 2, -3],
              [1, 1, 3]])
res = np.dot(x, y)
print(res)

[32 12 -9]

Example7: numpy.dot() product of complex vectors

In this, we have taken two complex vectors and calculated their dot product using the numpy.dot() function.

import numpy as np 
   
vector_x = 5 + 2j
vector_y = 2 + 3j
   
product = np.dot(vector_x, vector_y) 
print("Dot Product  : ", product) 

Dot Product  :  (4+19j)

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