Get Started With Jupyter Book

Leo Migdal

-Nov 17, 2025, 1:01 PM

Learn how to navigate this documentation and build your first book. Jupyter Book is a distribution of the MyST Document Engine. This documentation is for Jupyter Book 2. If you’re looking for the Jupyter Book 1 docs, go to jupyterbook.org/v1. Because Jupyter Book is a distribution of the MyST Document Engine, we keep most documentation about configuring and using Jupyter Book in the MyST User Guide. Anything you can do with MyST you can do with Jupyter Book - the only difference in using the two is the CLI verb (jupyter book vs.

myst). This User Guide steps you through high-level concepts, tutorials for step-by-step learning, and how-to guides to get things done. It focuses on use-cases for multi-document projects, like community knowledge bases and multi-page books.[1] Jupyter Notebook is an incredibly powerful tool for interactively developing and presenting data science projects. It combines code, visualizations, narrative text, and other rich media into a single document, creating a cohesive and expressive workflow. This guide will give you a step-by-step walkthrough on installing Jupyter Notebook locally and creating your first data project.

If you're new to Jupyter Notebook, we recommed you follow our split screen interactive Learn and Install Jupyter Notebook project to learn the basics quickly. At its core, a notebook is a document that blends code and its output seamlessly. It allows you to run code, display the results, and add explanations, formulas, and charts all in one place. This makes your work more transparent, understandable, and reproducible. Jupyter Notebooks have become an essential part of the data science workflow in companies and organizations worldwide. They enable data scientists to explore data, test hypotheses, and share insights efficiently.

As an open-source project, Jupyter Notebooks are completely free. You can download the software directly from the Project Jupyter website or as part of the Anaconda data science toolkit. Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter supports for over 40 different programming languages and Python is one of them. Python is a requirement (Python 3.3 or greater or Python 2.7) for installing Jupyter Notebook itself. Jupyter Notebook can be installed by using either of the two ways described below:

1. Install Jupyter Notebook with Anaconda Install Python and Jupyter using Anaconda Distribution, which includes Python, Jupyter Notebook and other commonly used packages for scientific computing and data science. To install Anaconda, go through How to install Anaconda on Windows and follow the instructions provided. Install Jupyter using PIP package manager used to install and manage software packages/libraries written in Python. To install pip, go through How to install PIP on Windows and follow the instructions provided.

Recommended Video CourseUsing Jupyter Notebooks Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Using Jupyter Notebooks The Jupyter Notebook is an open source web application that you can use to create and share documents that contain live code, equations, visualizations, and text. Jupyter Notebook is maintained by the people at Project Jupyter. Jupyter Notebooks are a spin-off project from the IPython project, which used to have an IPython Notebook project itself.

The name, Jupyter, comes from the core supported programming languages that it supports: Julia, Python, and R. Jupyter ships with the IPython kernel, which allows you to write your programs in Python, but there are currently over 100 other kernels that you can also use. Free Bonus: 5 Thoughts On Python Mastery, a free course for Python developers that shows you the roadmap and the mindset you’ll need to take your Python skills to the next level. Jupyter Notebook offers developers an interactive, customizable platform for displaying code and data, particularly for machine learning and data science projects. Notebooks integrate text, code and images into a centralized, easily shareable interface, making them a highly flexible tool for organizing and sharing information. This beginner's guide covers the basics of Jupyter Notebook, including how notebooks work, why they're useful, and steps for installing and using them.

Jupyter Notebook is a web-based interface similar to an integrated development environment (IDE) that lets users run code and display text and images. While Jupyter notebooks, like HTML pages, can be viewed in a web browser, Jupyter Notebook provides additional features not present in standard HTML, including the following: These features make Jupyter Notebook a popular tool for presenting machine learning projects to stakeholders. Instead of distributing project information across various formats -- for example, HTML and Word documents for text, with separate files for sample code and data -- machine learning engineers and data scientists can consolidate... Jupyter Notebook is developed by Project Jupyter, an open source initiative. Although Jupyter Notebook was originally the project's main product, the developers have since introduced JupyterLab, which offers a broader range of capabilities.

For example, JupyterLab users can also run terminals and create traditional text files directly within their browser. It's possible to create and interact with Jupyter notebooks without using JupyterLab, but working with notebooks inside JupyterLab grants access to these additional features. If you’re just starting with Jupyter Notebooks, welcome! This guide will walk you through everything you need to know about this powerful tool that’s transformed how people work with data and code. We’ll also look at how newer code editors are making notebooks even better. Imagine a digital notebook where you can write code, see the results immediately, and add explanations in plain English – that’s Jupyter Notebooks.

These .ipynb files are perfect for: The easiest way is using Anaconda (recommended for beginners): Then create a new notebook and type this in your first cell: Hit Shift+Enter to run it – your first notebook is alive! This document is a brief step-by-step tutorial on installing and running Jupyter (IPython) notebooks on local computer for new users who have no familiarity with python. Briefly, if someone gave you a notebook to run and you don’t know what a notebook is, this document is for you.

Jupyter Notebook App (formerly IPython Notebook) is an application running inside the browser. This guide describes how to install and use Jupyter Notebook App as normal desktop application, without using any remote server. For other use-cases, please refer to the Official Jupyter Documentation. The Next button will bring you to the first item in the tables of content (What is Jupyter Notebook). Jupyter Notebook is a versatile, free tool perfect for beginners entering the world of data science and programming. It supports over 40 programming languages and combines live code, equations, visualizations, and text in a single document.

Here's a quick guide to get you started: This guide is designed to make your start with Jupyter as smooth as possible, providing a comprehensive overview of its features and how to use them effectively. Jupyter is a free tool you can use on the internet to create and share documents that have live code, math problems, charts, and text. It started with the IPython project, which was about making a better way to write and run Python code. Later, it grew to include other languages like R and Julia, turning into what we now know as Jupyter. The main feature of Jupyter is the Jupyter Notebook.

This is a place where you can: Jupyter Notebook makes working with data easier and is very popular for data science. This guide is all about how to use Jupyter Notebook for your data projects. You may think of programming as typing code into an interpreter, text editor, or IDE. Enter Jupyter, a radically different way of programming that freely mixes text, code, and graphics into interactive documents. It's powerful but also easy to use.

Jupyter is a "notebook interface" that lets you create "literate programs," programs that mix code with explanatory text. Jupyter is an offshoot of the IPython project, which aims to create a better interactive mode for Python. While Jupyter incorporates IPython, it's still available as a standalone program. Despite the name, Jupyter doesn't support Python only. Python is one of the many language "kernels" you can use with it. You may have heard that good programs should have comments that explain anything that might confuse other people reading your code, or yourself a few months after you touched a piece of code, but...

You can create documents explaining what you're doing and mixing in graphics and code. Below is an example I created in my spare time using Python and Seaborn to plot the trend line of a dataset of the number of airline passengers from the late 1950s to the... It's an example of the kinds of things you can do. Jupyter is popular in scientific computing, especially in data analysis and data science, because it offers an easy way for researchers to share their results, but you don't have to be a scientist to... You can use Jupyter for ordinary coding projects and have an easy way to show what you're doing to other people. It's a different style of programming revolving around interactive documents rather than just running code.

Get Started With Jupyter Book

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1. Install Jupyter Notebook With Anaconda Install Python And Jupyter