This is the code repository for JupyterLab Quick Start Guide, published by Packt.
A practical approach to implementing blockchain in your enterprise
Jupyterlab is a web-based data science interface and natural evolution of Jupyter Notebooks. This guide will take you through the core commands and functionalities of JupyterLab. You will learn to customize and enhance your JupyterLab productivity by installing additional extensions.
This book covers the following exciting features:
- Install JupyterLab and work with Jupyter Notebooks
- Host JupyterLab Notebooks on GitHub and access GitHub resources in your Notebooks
- Explore different methods for exchanging Notebooks
- Discover ways in which multiple users can access the same Notebook
- Publish your Notebooks with nbviewer and convert them into different formats
- Attach and operate widgets within your Notebooks using a JupyterLab document
- Use JupyterLab to work collaboratively with multiple data scientists in your teams
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example,
The code will look like the following:
import neptune
neptune.init('shared/onboarding', api_token='eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vdWkubmVwdHVuZS5tbCIsImFwaV9rZXkiOiJiNzA2YmM4Zi03NmY5LTRjMmUtOTM5ZC00YmEwMzZmOTMyZTQifQ==')
with neptune.create_experiment():
neptune.append_tag('minimal-example')
Following is what you need for this book: 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.
With the following software and hardware list you can run all code files present in the book (Chapter 4-6).
| Chapter | Software required | OS required |
|---|---|---|
| 1-6 | Anaconda Distribution (JupyterLab) | Windows, Mac OS X, and Linux (Any) |
| 3-6 | Node.js | Windows, Mac OS X, and Linux (Any) |
| 5 | nteract | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
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.
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