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Learning Jupyter 5 - Second Edition

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Learning Jupyter 5

2.5 (2)

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Overview of this book

The Jupyter Notebook 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, and machine learning. Learning Jupyter 5 will help you get to grips with interactive computing using real-world examples. The book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next, you will learn to integrate the Jupyter system with different programming languages such as R, Python, Java, JavaScript, and Julia, and explore various versions and packages that are compatible with the Notebook system. Moving ahead, you will master interactive widgets and namespaces and work with Jupyter in a multi-user mode. By the end of this book, you will have used Jupyter with a big dataset and be able to apply all the functionalities you’ve explored throughout the book. You will also have learned all about the Jupyter Notebook and be able to start performing data transformation, numerical simulation, and data visualization.

Preface

Who this book is for

What this book covers

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Free Chapter

Introduction to Jupyter

First look at Jupyter

Installing Jupyter

Notebook structure

Notebook workflow

Basic Notebook operations

Security in Jupyter

Configuration options for Jupyter

Summary

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

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

R machine learning

Summary

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

Jupyter Java Coding

Adding the Java kernel to your installation

Jupyter Java console

Jupyter Java output

Java Optional

Java compiler errors

Java lambdas

Java Collections

Java summary statistics

Summary

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

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

Jupyter and Big Data

Apache Spark

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

Interactive Widgets

Installing widgets

Widget basics

Interact widget

Interactive widget

Widgets

Summary

Sharing and Converting Jupyter Notebooks

Sharing Notebooks

Converting Notebooks

Summary

Multiuser Jupyter Notebooks

A sample interactive Notebook

JupyterHub

Docker

Summary

What's Next?

JupyterHub

JupyterLab

Scale

Custom frontends

Interactive computing standards

Summary

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Jupyter JavaScript Coding

JavaScript is a high-level, dynamic, untyped, and interpreted programming language. There are several outgrowth languages that are based on JavaScript. In the case of Jupyter, the underlying JavaScript is really Node.js. Node.js is an event-based framework that uses JavaScript, which can be used to develop large, scalable applications. Note that this is in contrast to the earlier languages covered in this book which are primarily used for data analysis (Python is a general language as well, but has clear aspects that deal with its capabilities of performing data analysis).

In this chapter, we will cover the following topics: