Jupyter Cookbook E-Book

Dan Toomey

• Herausgeber: Packt Publishing
• Kategorie: Lebensstil
• Sprache: Englisch

Seitenzahl: 163

Veröffentlichungsjahr: 2018

Beschreibung

Jupyter has garnered a strong interest in the data science community of late, as it makes common data processing and analysis tasks much simpler. This book is for data science professionals who want to master various tasks related to Jupyter to create efficient, easy-to-share, scientific applications.
The book starts with recipes on installing and running the Jupyter Notebook system on various platforms and configuring the various packages that can be used with it. You will then see how you can implement different programming languages and frameworks, such as Python, R, Julia, JavaScript, Scala, and Spark on your Jupyter Notebook. This book contains intuitive recipes on building interactive widgets to manipulate and visualize data in real time, sharing your code, creating a multi-user environment, and organizing your notebook. You will then get hands-on experience with Jupyter Labs, microservices, and deploying them on the web.
By the end of this book, you will have taken your knowledge of Jupyter to the next level to perform all key tasks associated with it.

Leseprobe

Jupyter Cookbook

Over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more

Dan Toomey

BIRMINGHAM - MUMBAI

Jupyter Cookbook

Copyright © 2018 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Commissioning Editor: Pravin DhandreAcquisition Editor: Tushar GuptaContent Development Editor: Cheryl DsaTechnical Editor: Sagar SawantCopy Editor: Vikrant Phadke, Safis EditingProject Coordinator: Nidhi JoshiProofreader: Safis EditingIndexer: Rekha NairGraphics: Tania DuttaProduction Coordinator: Arvindkumar Gupta

First published: April 2018

Production reference: 1270418

Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK.

ISBN 978-1-78883-944-0

www.packtpub.com

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Contributors

About the author

Dan Toomey has been developing applications for over 20 years. He has worked in a variety of industries and companies of all sizes, in roles ranging from sole contributor to the VP/CTO level. For the last 10 years or so, he has been contracting with companies in the eastern Massachusetts area, under Dan Toomey Software Corp. Dan has also written a few books with Packt Publishing.

About the reviewers

Nikhil Borkar holds the CQF designation and a PG degree in Quantitative Finance from the University of Mumbai. He works as an independent fintech and legal consultant. Prior to this, he was with Morgan Stanley Capital International as a Global RFP Project Manager. He has worked on Quantitative Finance and economic research projects using R, Python, and Excel VBA. He loves to approach problems in a multidisciplinary, holistic way. He is actively working on machine learning, AI, and deep learning projects.

Nikhil Akki started his career in IT support, and within a couple of years shifted to sales and marketing (after pursing MBA). Nikhil works at Deloitte in Mumbai as data science consultant. He has hands-on experience in building enterprise-grade - NLP, machine learning, and recommender system-based applications for his clients. He is often found taking up MOOCs and Kaggle problems. Prior to his current role, he taught Business Statistics at postgraduate level in an upcoming B-School in South Mumbai.

Juan Tomás Oliva Ramos is an environmental engineer from the University of Guanajuato, Mexico, with a master's degree in administrative engineering and quality. He has over five years of experience in management and development of patents, technological innovation projects, and technological solutions through statistical control of processes. He has been a teacher of statistics, entrepreneurship, and technological development since 2011. He has developed prototypes via programming and automation technologies for improvement of operations, which have been registered for patents.

I want to thank God for giving me the wisdom and humility to review this book. I thank Rubén for inviting me to collaborate on this adventure. I want to thank my wife, Brenda, our two magic princesses (Maria Regina and Maria Renata) and Angel Tadeo), All of you are my strengths, happiness and my desire to look for the best for you.

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Table of Contents

Title Page

Copyright and Credits

Jupyter Cookbook

Packt Upsell

Why subscribe?

PacktPub.com

Contributors

About the author

About the reviewers

Packt is searching for authors like you

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

Installation and Setting up the Environment

Introduction

Installing Jupyter on Windows

Getting ready

How to do it...

Installing Jupyter directly

Installing Jupyter through Anaconda

Installing Jupyter on the Mac

Getting ready

How to do it...

Installing Jupyter on the Mac via Anaconda

Installing Jupyter on the the Mac via the command line

Installing Jupyter on Linux

How to do it...

Installing Jupyter on a server

How to do it...

Example Notebook with a user data collision

Adding an Engine

Introduction

Adding the Python 3 engine

How to do it...

Installing the Python 3 engine

Running a Python 3 script

Adding the R engine

How to do it...

Installing the R engine using Anaconda Navigator

Installing the R engine via command line

Running an R Script

Adding the Julia engine

How to do it...

Installing the Julia engine

Running a Julia script

Adding the JavaScript engine

How to do it...

Installing the Node.JS engine

Running a Node.JS script

Adding the Scala engine

How to do it...

Installing the Scala engine

Running a Scala script

Adding the Spark engine

How to do it...

Installing the Spark engine

Running a Spark script

Accessing and Retrieving Data

Introduction

Reading CSV files

Getting ready

How to do it...

How it works...

Reading JSON files

Getting ready

How to do it...

How it works...

Accessing a database

Getting ready

How to do it...

How it works...

Reading flat files

Getting ready

How to do it...

How it works...

Reading text files

Getting ready

How to do it...

How it works...

Visualizing Your Analytics

Introduction

Generating a line graph using Python

How to do it...

How it works...

Generating a histogram using Python

How to do it...

How it works...

Generating a density map using Python

How to do it...

How it works...

Plotting 3D data using Python

How to do it...

How it works...

Present a user-interactive graphic using Python

How to do it...

How it works...

Visualizing with R

How to do it...

How it works...

Generate a regression line of data using R

How to do it...

How it works...

Generate an R lowess line graph

How to do it...

How it works...

Producing a Scatter plot matrix using R

How to do it...

How it works...

Producing a bar chart using R

How to do it...

How it works...

Producing a word cloud using R

How to do it...

How it works...

Visualizing with Julia

Getting ready

How to do it...

Drawing a Julia scatter diagram of Iris data using Gadfly

How to do it...

Drawing a Julia histogram using Gadfly

How to do it...

How it works...

Drawing a Julia line graph using the Winston package

How to do it...

How it works...

Working with Widgets

Introduction

What are widgets?

Getting ready

How to do it...

How it works...

Using ipyleaflet widgets

Getting ready

How to do it...

How it works...

Using ipywidgets

Getting ready

How to do it...

How it works...

Using a widget container

How to do it...

Using an interactive widget

How to do it...

How it works...

Using an interactive text widget

How to do it...

How it works...

Linking widgets together

How to do it...

How it works...

Another ipywidgets linking example

How to do it...

How it works...

Using a cookie cutter widget

Getting ready

How to do it...

How it works...

Developing an OPENGL widget

Getting ready

Creating a simple orbit of one object

How to do it...

How it works...

Using a complex orbit of multiple objects

How to do it...

How it works...

Jupyter Dashboards

Introduction

What is Jupyter dashboards?

Getting ready

How to do it...

There's more...

Creating an R dashboard

How to do it...

How it works...

Create a Python dashboard

How to do it...

Creating a Julia dashboard

How to do it...

Develop a JavaScript (Node.js) dashboard

How to do it...

Sharing Your Code

Introduction

Sharing your Notebook using server software

Using a Notebook server

How to do it...

Using web encryption for your Notebook

Using a web server

How to do it...

Sharing your Notebook through a public server

How to do it...

Sharing your Notebook through Docker

How to do it...

Sharing your Notebook using nbviewer

How to do it...

Converting your Notebook into a different format

How to do it...

Converting Notebooks to R

How to do it...

How it works...

Converting Notebooks to HTML

How to do it...

How it works...

Converting Notebooks to Markdown

How to do it...

How it works...

Converting Notebooks to reStructedText

How to do it...

How it works...

Converting Notebooks to Latex

How to do it...

How it works...

Converting Notebooks to PDF

How to do it...

How it works...

Multiuser Jupyter

Introduction

Why multiuser?

How to do it...

How it works...

Providing multiuser with JupyterHub

Getting ready

How to do it...

Providing multiuser with Docker

Getting ready

How to do it...

Running your Notebook in Google Cloud Platform

Getting ready

Set up your GC project

Create a Cloud storage bucket

Create a cluster

Install Jupyter

Download the script

Execute the script

Configure Jupyter

How to do it...

Next steps

There's more...

Running your Notebook in AWS

Getting ready

How to do it...

How it works...

There's more...

Running your Notebook in Azure

Getting ready

How to do it...

How it works...

There's more...

Interacting with Big Data

Introduction

Obtaining a word count from a big-text data source

How to do it...

How it works...

Obtaining a sorted word count from a big-text source

How to do it...

How it works...

Examining big-text log file access

How to do it...

How it works...

Computing prime numbers using parallel operations

How to do it...

How it works...

Analyzing big-text data

How to do it...

How it works...

Analyzing big data history files

How to do it...

How it works...

Jupyter Security

Introduction

How much risk?

Known vulnerabilities

Web attack strategies

Inherent Jupyter security issues

Security mechanisms built into Jupyter

How to do it...

Token-based authentication

Password authentication

No authentication

Using SSL

How to do it...

Creating an SSL certificate

Apply the SSL certificate

The Jupyter trust model

How to do it...

Trust overrides

Collaboration

Controlling network access

How to do it...

Controlling domain access

Controlling IP access

Additional practices

How to do it...

Server IP address

URL prefix

No browser

Jupyter Labs

Introduction

JupyterLab features

Installing and starting JupyterLab

How to do it...

Installing JupyterLab

Starting JupyterLab

JupyterLab display

How to do it...

JupyterLab menus

How to do it...

Starting a Notebook

How to do it...

Starting a console

Preface

Jupyter has garnered strong interest in the data science community of late, as it makes common data processing and analysis tasks much simpler. This book is for data science professionals who want to master various tasks related to Jupyter to create efficient, easy-to-share scientific applications.

The book starts with recipes on installing and running the Jupyter Notebook system on various platforms and configuring the various packages that can be used with it. You will then see how you can implement different programming languages and frameworks on your Jupyter Notebook, such as Python, R, Julia, JavaScript, Scala, and Spark. This book contains intuitive recipes on building interactive widgets to manipulate and visualize data in real time, sharing your code, creating a multi-user environment, and organizing your Notebook. You will then get hands-on experience with JupyterLabs, microservices, and deploying them on the Web.

By the end of this book, you will have taken your knowledge of Jupyter to the next level to perform all key tasks associated with it.

Who this book is for

This cookbook is for data science professionals, developers, technical data analysts, and programmers who want to execute technical coding, visualize output, and do scientific computing with one tool. Prior understanding of data science concepts will be helpful for using this book, but it's not mandatory.

What this book covers

Chapter 1, Installation and Setting up the Environment, teaches you how to install Jupyter on different environments, such as Windows, macOS, Linux, and a server machine.

Chapter 2, Adding an Engine, shows you the steps to add these engines to your Jupyter installation so that you can script your Notebook in the language you like.

Chapter 3, Accessing and Retrieving Data, teaches how to access and retrieve data from files in different formats in Jupyter.

Chapter 4, Visualize Your Analytics, covers recipes for visualizing your analytics in Python, R, and Julia.

Chapter 5, Working with Widgets, describes the wide range of possibilities of widgets in Jupyter.

Chapter 6, Jupyter Dashboards, teaches how to install and enable Jupyter dashboards layout extension to your Notebook. 

Chapter 7,Sharing Your Code, shows you several methods for sharing your Notebook with others, including using different software packages and converting the Notebook into a different form for readers without access to Jupyter.

Chapter 8, Multiuser Jupyter, explores several options for enabling Jupyter Notebooks as a multiuser platform.

Chapter 9, Interacting with Big Data, covers the methods of accessing big data from Jupyter.

Chapter 10, Jupyter Security, investigates the various security mechanisms available for your Jupyter Notebook.

Chapter 11, Jupyter Labs, lets us try new features of Jupyter in a lab environment to create our own sample Notebook.

To get the most out of this book

This book is focused on using Jupyter as a platform for data science. It assumes that you have a good understanding of data science concepts and are looking to use Jupyter as your presentation platform.

Download the example code files

You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

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Select the

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Enter the name of the book in the

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Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

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The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Jupyter-Cookbook. If there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/JupyterCookbook_ColorImages.pdf.