Get to grips with the basics of JupyterLab and its web interface with the help of this quick start guide

Key FeaturesManage JupyterLab kernels, code consoles, and terminals, and share your work over the cloudOrganize your data solutions within JupyterLabInstall and configure extensions on JupyterLab using easy-to-follow examplesBook Description

JupyterLab is a web-based interface and the natural evolution of Jupyter Notebook. This guide will take you through the core commands and functionalities of JupyterLab and help you enhance your JupyterLab productivity.

Starting with the installation of JupyterLab, this book will give you an overview of its features and the variety of problems it solves. You’ll see how you can work with external files inside the platform, and understand how to use the code console and terminal. This will help you have distinct control over the scripts you work with. As you progress, you’ll get to grips with the extensions available in JupyterLab, and gain insights into adding extensions to introduce new functionality in the interface. This book also covers widget operations within your document, different design patterns in JupyterLab, and the various methods for exchanging Notebooks. Additionally, you’ll explore how to host JupyterLab Notebooks on GitHub.

By the end of this Jupyter book, you’ll have become well-versed with all the components of JupyterLab and be able to use it collaboratively within your data science teams.

What you will learnInstall JupyterLab and work with Jupyter NotebooksHost JupyterLab Notebooks on GitHub and access GitHub resources in your NotebooksExplore different methods for exchanging NotebooksDiscover ways in which multiple users can access the same NotebookPublish your Notebooks with nbviewer and convert them into different formatsAttach and operate widgets within your Notebooks using a JupyterLab documentUse JupyterLab to work collaboratively with multiple data scientists in your teamsWho this book is for

This book is for data scientists and data analysts who are new to JupyterLab as well as for existing Jupyter users who want to get acquainted with its impressive features. Although not necessary, basic knowledge of Python will be helpful.

Lindsay Richman is a Product Manager who has worked in product, analytics and consulting within a variety of industries. She is passionate about the Jupyter project, and JupyterLab’s role in democratizing scientific computing. She wrote Ch. 5 for this book; proceeds from her chapter will be donated to NumFocus. Melissa Ferrari completed her Ph.D. in physics at New York University. Jupyter has been a pivotal tool in her research as a method for exploratory data analysis (especially with interactive widgets), prototyping data analysis pipelines, interactive modeling, and adhering to scientific reproducibility and transparency standards. Joseph Oladokun is a Data Scientist at eHealth Africa in Nigeria, where he has an in-depth understanding of advanced techniques and tools needed to generate insights from data using the best practices with his experience in data analytics, engineering, and machine learning. Joseph is also a leader and mentor for various data science communities in Africa, and he is the founder of Data Science in Africa, an organization that uses the information to empower data scientists in Africa. He's also the co-lead of Africa R Users Group. Beyond his profession, Joseph is a leader who is very passionate about sharing information and ideas with others. Wesley Banfield is an R&D Geologist with a passion for digital innovation. He has worked in tech companies leveraging his software development skills and geological background to provide novel solutions. Throughout his career, his go-to tool for innovation has been Jupyter. Dan Toomey has been developing application software for over 20 years. He has worked in a variety of industries and company sizes in roles from a sole contributor on a project to VP/CTO overseeing and directing many. Dan had been a contract software developer for years, again working at different levels typically in the Java space. For the last several years Dan has been an employee of different companies in the eastern Massachusetts area. Dan has also written R for Data Sciences, Introduction to Jupyter (version 1 and 2), Jupyter for Data Sciences and the Jupyter Cookbook.

Lindsay Richman

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