Compute the inner product of vectors for 1-D arrays using NumPy in Python Last Updated : 29 Aug, 2020 Summarize Suggest changes Share Like Article Like Report Python has a popular package called NumPy which used to perform complex calculations on 1-D and multi-dimensional arrays. To find the inner product of two arrays, we can use the inner() function of the NumPy package. Syntax: numpy.inner(array1, array2) Parameters: array1, array2: arrays to be evaluated Returns: Inner Product of two arrays Example 1: Python3 # Importing library import numpy as np # Creating two 1-D arrays array1 = np.array([6,2]) array2 = np.array([2,5]) print("Original 1-D arrays:") print(array1) print(array2) # Output print("Inner Product of the two array is:") result = np.inner(array1, array2) print(result) Output: Original 1-D arrays: [6 2] [2 5] Inner Product of the two array is: 22 Example 2: Python3 # Importing library import numpy as np # Creating two 1-D arrays array1 = np.array([1,3,5]) array2 = np.array([0,1,5]) print("Original 1-D arrays:") print(array1) print(array2) # Output print("Inner Product of the two array is:") result = np.inner(array1, array2) print(result) Output: Original 1-D arrays: [1 3 5] [0 1 5] Inner Product of the two array is: 28 Example 3: Python3 # Importing library import numpy as np # Creating two 1-D arrays array1 = np.array([1,2,2,8]) array2 = np.array([2,1,0,6]) print("Original 1-D arrays:") print(array1) print(array2) # Output print("Inner Product of the two array is:") result = np.inner(array1, array2) print(result) Output: Original 1-D arrays: [1 2 2 8] [2 1 0 6] Inner Product of the two array is: 52 Advertise with us Next Article How to compute the cross product of two given vectors using NumPy? K kartik Follow Similar Reads Get the Outer Product of an array with vector of letters using NumPy in Python In this article let's see how to get the outer product of an array with a vector of letters in Python. numpy.outer() method The numpy.outer() method is used to get the outer product of an array with a vector of elements in Python. A matrix is the outer product of two coordinate vectors in linear alg 4 min read How to compute the cross product of two given vectors using NumPy? Let's see the program to compute the cross product of two given vectors using NumPy. For finding the cross product of two given vectors we are using numpy.cross() function of NumPy library.Syntax: numpy.cross(a, b, axisa=-1, axisb=-1, axisc=-1, axis=None)[Return: cross product of two (arrays of) vec 1 min read Make grid for computing a Mandelbrot set with outer product using NumPy in Python In this article let's learn how to make a grid for computing a Mandelbrot set with an outer product in Numpy using Python. numpy.outer() method: In Python, the numpy.outer() method is used to acquire the outer product of an array and a vector of elements. In linear algebra, a matrix is the outer pr 4 min read Compute the determinant of a given square array using NumPy in Python In Python, the determinant of a square array can be easily calculated using the NumPy package. This package is used to perform mathematical calculations on single and multi-dimensional arrays. numpy.linalg is an important module of NumPy package which is used for linear algebra. We can use det() fun 2 min read Python Program to Get dot product of multidimensional Vectors using NumPy Given two multidimensional Vectors, the task is to write a Python program to get the dot product of two multidimensional Vectors using NumPy.Example: Lets take 2 vectors a = [2,5,3] and b = [6,3,1]Dot Product(ab) = (a[0] * b[0])+ (a[1] * b[1]) + (a[2] * b[2]) = (2*6)+ (5*3) + (3*1) = 30Dot Product i 3 min read Compute the Kronecker product of two multidimension NumPy arrays Given an m X n matrix A and a p X q matrix B, their Kronecker product is A ⊗ B, also called their matrix direct product, is an (m*p) X (n*q) matrix. A = | (a00)  (a01) |     | (a10)  (a11) | B = | (b00)  (b01) |     | (b10)  (b11) | A ⊗ B = | (a00)*(b00)  (a00)*(b01)  (a01)*(b00)  (a01)*(b00) | 2 min read Article Tags : Numpy Python-numpy Like