How to Perform Arithmetic Operations in NumPy Using Python?

Check out this tutorial that simplifies arithmetic operations in NumPy using Python. Dive into a guide, packed with examples and practical tips.| ProjectPro

NumPy is a powerful library in the Python ecosystem that provides support for large, multi-dimensional arrays and matrices, along with a plethora of mathematical functions to operate on these arrays. This hands-on tutorial will help you understand everything about the NumPy arithmetic operations, exploring the basic operations, functions, and statistical capabilities it offers to Python developers.

Before diving into arithmetic or numerical operations using NumPy, it's crucial to grasp the fundamental concept of NumPy arrays. NumPy arrays are homogeneous, multi-dimensional data structures that can store elements of the same data type. They come in the form of vectors, matrices, or higher-dimensional arrays, providing a flexible and efficient way to handle numerical data in Python. 

What are Basic Arithmetic Operations on NumPy Arrays in Python? 

NumPy simplifies arithmetic operations on arrays, enabling developers to perform element-wise operations with ease. Here are some basic operations:- 

NumPy Array Addition

NumPy Array Subtraction

NumPy Array Multiplication

NumPy Array Division

Mathematical NumPy Functions in Python with Examples 

NumPy provides a plethora of mathematical functions to perform complex operations on arrays efficiently. Let's explore some of these functions:

Finding Square Root in NumPy

Finding Exponential Function in NumPy

Finding Logarithm in NumPy

Statistical Functions in NumPy with Examples 

NumPy also caters to the needs of data scientists and statisticians with its rich set of statistical functions. Here are a few examples:

Finding Mean of a NumPy Array

Finding Standard Deviation of NumPy Array

Finding sum of the NumPy Array

Example - Applying Statistical Functions on a NumPy Array 

Step 1 - Loading Library

We have imported numpy which is needed.

       import numpy as np

Step 2 - Creating a NumPy Array

We have created a numpy array.

    sales = np.array([4352, 233, 3245, 256, 2394])

    print(sales)

Step 3 - Numerical analysis

We have calculated the mean, minimum, maximum and sum of the values in the array.

    print(sales.mean())

    print(sales.sum())

    print(sales.min())

    print(sales.max())

So the output comes as

[4352  233 3245  256 2394]

2096.0

10480

233

4352

Advanced Arithmetic Operations on Arrays in Python

NumPy also facilitates advanced operations on NumPy arrays. Check out them below - 

Broadcasting in NumPy

  1. Matrix Multiplication 

Matrix Multiplication

Element–Wise Power in NumPy

Element-Wise Comparison

Get Your Hands on NumPy Operations with ProjectPro! 

NumPy arithmetic operations in Python opens up a world of possibilities for efficient numerical computing. We've explored the basics, statistical functions, and advanced operations that NumPy offers, showcasing its power in handling arrays and matrices. However, true magic happens when you put your knowledge into practice. ProjectPro, with its vast repository of over 270+ projects in data science and big data, provides the perfect platform to transform theoretical understanding into practical expertise. It's not just about reading and learning; it's about experiencing the thrill of real-world applications. ProjectPro helps you to get your hands-on with NumPy and other essential tools, making the learning journey not only educational but also exhilarating. Don't just stop at understanding – take the plunge into practical application with ProjectPro and elevate your skills to new heights! 

Download Materials

What Users are saying..

profile image

Gautam Vermani

Data Consultant at Confidential
linkedin profile url

Having worked in the field of Data Science, I wanted to explore how I can implement projects in other domains, So I thought of connecting with ProjectPro. A project that helped me absorb this topic... Read More

Relevant Projects

Build a Review Classification Model using Gated Recurrent Unit
In this Machine Learning project, you will build a classification model in python to classify the reviews of an app on a scale of 1 to 5 using Gated Recurrent Unit.

Learn How to Build a Linear Regression Model in PyTorch
In this Machine Learning Project, you will learn how to build a simple linear regression model in PyTorch to predict the number of days subscribed.

Time Series Forecasting with LSTM Neural Network Python
Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.

Mastering A/B Testing: A Practical Guide for Production
In this A/B Testing for Machine Learning Project, you will gain hands-on experience in conducting A/B tests, analyzing statistical significance, and understanding the challenges of building a solution for A/B testing in a production environment.

Time Series Forecasting Project-Building ARIMA Model in Python
Build a time series ARIMA model in Python to forecast the use of arrival rate density to support staffing decisions at call centres.

MLOps AWS Project on Topic Modeling using Gunicorn Flask
In this project we will see the end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable machine learning models by using AWS

NLP Project for Multi Class Text Classification using BERT Model
In this NLP Project, you will learn how to build a multi-class text classification model using using the pre-trained BERT model.

Build an End-to-End AWS SageMaker Classification Model
MLOps on AWS SageMaker -Learn to Build an End-to-End Classification Model on SageMaker to predict a patient’s cause of death.

Build Regression (Linear,Ridge,Lasso) Models in NumPy Python
In this machine learning regression project, you will learn to build NumPy Regression Models (Linear Regression, Ridge Regression, Lasso Regression) from Scratch.

Build an Image Segmentation Model using Amazon SageMaker
In this Machine Learning Project, you will learn to implement the UNet Architecture and build an Image Segmentation Model using Amazon SageMaker