Arithmetic operations perform in an array using numpy | S-Logix

How to perform arithmetic operation in an array using numpy

Arithmetic Operation in Array using NumPy

Condition for Performing Arithmetic Operations in an Array using NumPy

Description:
NumPy provides efficient ways to perform arithmetic operations on arrays, allowing element-wise operations on large datasets.

These operations include basic arithmetic like addition, subtraction, multiplication, and division, as well as more advanced operations like squaring, power functions, and trigonometric functions.

Step-by-Step Process

Import NumPy:
Import the NumPy library.
Create Arrays:
Create NumPy arrays using np.array().
Perform Basic Arithmetic Operations:
Addition, Subtraction, Multiplication, and Division between arrays or a scalar.
Element-wise Operations:
Perform element-wise mathematical operations like square, power, or logarithm using NumPy functions.
Scalar Operations:
Apply operations between arrays and scalar values.

Sample Source Code

# Code for Arithmetic Operation in Array using NumPy

import numpy as np

arr1 = np.array([1, 2, 3, 4, 5])

arr2 = np.array([6, 7, 8, 9, 10])

# 1. Addition of two arrays
add_result = arr1 + arr2
print("\nAddition of arr1 and arr2:", add_result)

# 2. Subtraction of two arrays
sub_result = arr1 - arr2
print("\nSubtraction of arr1 and arr2:", sub_result)

# 3. Multiplication of two arrays
mul_result = arr1 * arr2
print("\nMultiplication of arr1 and arr2:", mul_result)

# 4. Division of two arrays
div_result = arr1 / arr2
print("\nDivision of arr1 by arr2:", div_result)

# 5. Scalar addition (Adding 10 to each element of arr1)
scalar_add = arr1 + 10
print("\nAddition of 10 to each element of arr1:", scalar_add)

# 6. Scalar multiplication (Multiplying each element of arr1 by 2)
scalar_mul = arr1 * 2
print("\nMultiplication of each element of arr1 by 2:", scalar_mul)

# 7. Element-wise power (arr1 raised to the power of 2)
power_result = np.power(arr1, 2)
print("\nEach element of arr1 raised to the power of 2:", power_result)

# 8. Element-wise square root (Square root of each element of arr2)
sqrt_result = np.sqrt(arr2)
print("\nSquare root of each element in arr2:", sqrt_result)

# 9. Trigonometric operation (Sine of each element in arr1, assuming radians)
sin_result = np.sin(arr1)
print("\nSine of each element in arr1:", sin_result)

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