How to implement element-wise addition,subtraction and multiplication in Numpy?

              Array is a collection of similar data elements. When you implement the arithmetic operations, you have to do each element. Let us implement this as follows.

Element-wise Arithmetic Operations in Numpy:

              It starts from importing numpy library. First, two arrays are included as input. Element wise operations are performed and result is printed.

Code:

import numpy as np1

# Two arrays as input

s_arr1 = np1.array([11, 22, 33, 44])

s_arr2 = np1.array([5, 10, 15, 20])

# Arithmetic operations on element wise

print("Element wise Arithmetic Operations:")

print("Addition:", s_arr1 + s_arr2)       

print("Subtraction:", s_arr2 - s_arr1)     print("Multiplication:", s_arr1 * s_arr2) 

Output:

Element wise Arithmetic Operations:

Addition: [16 32 48 64]

Subtraction: [ -6 -12 -18 -24]

Multiplication: [ 55 220 495 880]

Element‑wise operations differ from matrix multiplication:

              This program gives you the difference from Matrix multiplication.

Code:

import numpy as np1

# input of two 2D arrays (matrices)

A = np1.array([[4, 5],

              [6, 7]])

B = np1.array([[1, 2],

              [3, 4]])

# Element-wise Arithmetic operations

print("Addition:\n", A + B)

print("Subtraction:\n", B - A)

print("Element-wise Multiplication:\n", A * B)

# Matrix multiplication (dot product)

print("Matrix Multiplication:\n", np1.dot(A, B))

Output:

Addition:

 [[ 5  7]

 [ 9 11]]

Subtraction:

 [[-3 -3]

 [-3 -3]]

Element-wise Multiplication:

 [[ 4 10]

 [18 28]]

Matrix Multiplication:

 [[19 28]

 [27 40]]

These are the ways to implement the element-wise operations in arrays. Hope, you understood the concept. Keep coding!!!