Learning Jupyter

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

Learning Jupyter

Learning
Dan Toomey

2 customer reviews
Learn how to write code, mathematics, graphics, and output, all in a single document, as well as in a web browser using Project Jupyter
$39.99
$49.99
RRP $39.99
RRP $49.99
eBook
Print + eBook

Instantly access this course right now and get the skills you need in 2017

With unlimited access to a constantly growing library of over 4,000 eBooks and Videos, a subscription to Mapt gives you everything you need to learn new skills. Cancel anytime.

Preview in Mapt

Book Details

ISBN 139781785884870
Paperback238 pages

Book Description

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 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. Moving ahead, you master interactive widgets, namespaces, and working with Jupyter in a multiuser mode.

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

Table of Contents

Chapter 1: Introduction to Jupyter
First look at Jupyter
Installing Jupyter on Windows
Installing Jupyter on Mac
Notebook structure
Notebook workflow
Basic notebook operations
Security in Jupyter
Configuration options for Jupyter
Summary
Chapter 2: Jupyter Python Scripting
Basic Python in Jupyter
Python data access in Jupyter
Python pandas in Jupyter
Python graphics in Jupyter
Python random numbers in Jupyter
Summary
Chapter 3: Jupyter R Scripting
Adding R scripting to your installation
Basic R in Jupyter
R dataset access
R visualizations in Jupyter
R cluster analysis
R forecasting
Summary
Chapter 4: Jupyter Julia Scripting
Adding Julia scripting to your installation
Basic Julia in Jupyter
Julia limitations in Jupyter
Standard Julia capabilities
Julia visualizations in Jupyter
Julia Vega plotting
Julia parallel processing
Julia control flow
Julia regular expressions
Julia unit testing
Summary
Chapter 5: Jupyter JavaScript Coding
Adding JavaScript scripting to your installation
JavaScript Hello World Jupyter Notebook
Basic JavaScript in Jupyter
JavaScript limitations in Jupyter
Node.js d3 package
Node.js stats-analysis package
Node.js JSON handling
Node.js canvas package
Node.js plotly package
Node.js asynchronous threads
Node.js decision-tree package
Summary
Chapter 6: Interactive Widgets
Installing widgets
Widget basics
Interact widget
Interactive widget
Widgets
Summary
Chapter 7: Sharing and Converting Jupyter Notebooks
Sharing notebooks
Converting notebooks
Summary
Chapter 8: Multiuser Jupyter Notebooks
Sample interactive notebook
JupyterHub
Docker
Summary
Chapter 9: Jupyter Scala
Installing the Scala kernel
Scala data access in Jupyter
Scala array operations
Scala random numbers in Jupyter
Scala closures
Scala higher-order functions
Scala pattern matching
Scala case classes
Scala immutability
Scala collections
Named arguments
Scala traits
Summary
Chapter 10: Jupyter and Big Data
Apache Spark
Our first Spark script
Spark word count
Sorted word count
Estimate Pi
Log file examination
Spark primes
Spark text file analysis
Spark - evaluating history data
Summary

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

Authors

Table of Contents

Chapter 1: Introduction to Jupyter
First look at Jupyter
Installing Jupyter on Windows
Installing Jupyter on Mac
Notebook structure
Notebook workflow
Basic notebook operations
Security in Jupyter
Configuration options for Jupyter
Summary
Chapter 2: Jupyter Python Scripting
Basic Python in Jupyter
Python data access in Jupyter
Python pandas in Jupyter
Python graphics in Jupyter
Python random numbers in Jupyter
Summary
Chapter 3: Jupyter R Scripting
Adding R scripting to your installation
Basic R in Jupyter
R dataset access
R visualizations in Jupyter
R cluster analysis
R forecasting
Summary
Chapter 4: Jupyter Julia Scripting
Adding Julia scripting to your installation
Basic Julia in Jupyter
Julia limitations in Jupyter
Standard Julia capabilities
Julia visualizations in Jupyter
Julia Vega plotting
Julia parallel processing
Julia control flow
Julia regular expressions
Julia unit testing
Summary
Chapter 5: Jupyter JavaScript Coding
Adding JavaScript scripting to your installation
JavaScript Hello World Jupyter Notebook
Basic JavaScript in Jupyter
JavaScript limitations in Jupyter
Node.js d3 package
Node.js stats-analysis package
Node.js JSON handling
Node.js canvas package
Node.js plotly package
Node.js asynchronous threads
Node.js decision-tree package
Summary
Chapter 6: Interactive Widgets
Installing widgets
Widget basics
Interact widget
Interactive widget
Widgets
Summary
Chapter 7: Sharing and Converting Jupyter Notebooks
Sharing notebooks
Converting notebooks
Summary
Chapter 8: Multiuser Jupyter Notebooks
Sample interactive notebook
JupyterHub
Docker
Summary
Chapter 9: Jupyter Scala
Installing the Scala kernel
Scala data access in Jupyter
Scala array operations
Scala random numbers in Jupyter
Scala closures
Scala higher-order functions
Scala pattern matching
Scala case classes
Scala immutability
Scala collections
Named arguments
Scala traits
Summary
Chapter 10: Jupyter and Big Data
Apache Spark
Our first Spark script
Spark word count
Sorted word count
Estimate Pi
Log file examination
Spark primes
Spark text file analysis
Spark - evaluating history data
Summary

Book Details

ISBN 139781785884870
Paperback238 pages
Read More
From 2 reviews

Read More Reviews