Learning Jupyter

Learn to write code, mathematics, graphics, and output, all in a single document as well as in a web browser using IPython's Project Jupyter

About This Book

• Learn to write, execute, and comment your live code and formulae all under one roof using this unique guide
• This one-stop solution on Project Jupyter will teach you everything you need to know to perform scientific computation with ease
• This easy-to-follow, highly practical guide lets you forget your worries in scientific application development by leveraging big data tools such as Apache Spark, Python, R etc

This book caters to all developers, students, or educators who want to execute code, see output, and comment all in the same document, in the browser. Data science professionals will also find this book very useful to perform technical and scientific computing in a graphical, agile manner.

What You Will Learn

• Install and run the Jupyter Notebook system on your machine
• Implement programming languages such as R, Python, Julia, and JavaScript with Jupyter Notebook
• Use interactive widgets to to manipulate and visualize data in real time
• Start sharing your Notebook with colleagues
• Invite your colleagues to work with you in the same Notebook
• Organize your Notebook using Jupyter namespaces
• Access big data in Jupyter

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, machine learning, and much more.

This book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next we'll help you master interactive widgets, namespaces, and working with Jupyter in a multiuser mode. Moving ahead, you will learn to integrate Jupyter system with different programming languages such as R, Python, JavaScript, and Julia and explore the various versions and packages that are compatible with the Notebook system.

Towards the end, you will use Jupyter with a big data set and will apply all the functionalities learned throughout the book.