Jupyter cookbook : over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more / Dan Toomey.

Format:

Book

Author/Creator:

Toomey, Dan, author.

Language:

English

Subjects (All):

Command languages (Computer science).

Physical Description:

1 online resource (238 pages)

Edition:

1st edition

Place of Publication:

Birmingham ; Mumbai : Packt, [2018]

System Details:

text file

Summary:

Leverage the power of the popular Jupyter notebooks to simplify your data science tasks without any hassle About This Book Create and share interactive documents with live code, text and visualizations Integrate popular programming languages such as Python, R, Julia, Scala with Jupyter Develop your widgets and interactive dashboards with these innovative recipes 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 in one tool. Prior understanding of data science concepts will be helpful, but not mandatory, to use this book. What You Will Learn Install Jupyter and configure engines for Python, R, Scala and more Access and retrieve data on Jupyter Notebooks Create interactive visualizations and dashboards for different scenarios Convert and share your dynamic codes using HTML, JavaScript, Docker, and more Create custom user data interactions using various Jupyter widgets Manage user authentication and file permissions Interact with Big Data to perform numerical computing and statistical modeling Get familiar with Jupyter's next-gen user interface - JupyterLab In Detail 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. Style and approach The recipes in this book are highly practical and very easy to follow, and include tips and tricks that will help you crack any problem that you might com...

Contents:

Cover

Title Page

Copyright and Credits

Packt Upsell

Contributors

Table of Contents

Preface

Chapter 1: 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

Installing Jupyter on the Mac via Anaconda

Installing Jupyter on the the Mac via the command line

Installing Jupyter on Linux

Installing Jupyter on a server

Example Notebook with a user data collision

Chapter 2: Adding an Engine

Adding the Python 3 engine

Installing the Python 3 engine

Running a Python 3 script

Adding the R engine

Installing the R engine using Anaconda Navigator

Installing the R engine via command line

Running an R Script

Adding the Julia engine

Installing the Julia engine

Running a Julia script

Adding the JavaScript engine

Installing the Node.JS engine

Running a Node.JS script

Adding the Scala engine

Installing the Scala engine

Running a Scala script

Adding the Spark engine

Installing the Spark engine

Running a Spark script

Chapter 3: Accessing and Retrieving Data

Reading CSV files

How it works...

Reading JSON files

Accessing a database

Reading flat files

Reading text files

Chapter 4: Visualizing Your Analytics.

Introduction

Generating a line graph using Python

Generating a histogram using Python

Generating a density map using Python

Plotting 3D data using Python

Present a user-interactive graphic using Python

Visualizing with R

Generate a regression line of data using R

Generate an R lowess line graph

Producing a Scatter plot matrix using R

Producing a bar chart using R

Producing a word cloud using R

Visualizing with Julia

Drawing a Julia scatter diagram of Iris data using Gadfly

Drawing a Julia histogram using Gadfly

Drawing a Julia line graph using the Winston package

Chapter 5: Working with Widgets

What are widgets?

Using ipyleaflet widgets

Using ipywidgets

Using a widget container

Using an interactive widget

Using an interactive text widget

Linking widgets together

Another ipywidgets linking example

Using a cookie cutter widget

How it works.

Developing an OPENGL widget

Creating a simple orbit of one object

Using a complex orbit of multiple objects

Chapter 6: Jupyter Dashboards

What is Jupyter dashboards?

There's more...

Creating an R dashboard

Create a Python dashboard

Creating a Julia dashboard

Develop a JavaScript (Node.js) dashboard

Chapter 7: Sharing Your Code

Sharing your Notebook using server software

Using a Notebook server

Using web encryption for your Notebook

Using a web server

Sharing your Notebook through a public server

Sharing your Notebook through Docker

Sharing your Notebook using nbviewer

Converting your Notebook into a different format

Converting Notebooks to R

Converting Notebooks to HTML

Converting Notebooks to Markdown

Converting Notebooks to reStructedText

Converting Notebooks to Latex

Converting Notebooks to PDF

Chapter 8: Multiuser Jupyter

Why multiuser?

Providing multiuser with JupyterHub

Providing multiuser with Docker

Running your Notebook in Google Cloud Platform

Set up your GC project

Create a Cloud storage bucket

Create a cluster.

Install Jupyter

Download the script

Execute the script

Configure Jupyter

Next steps

Running your Notebook in AWS

Running your Notebook in Azure

Chapter 9: Interacting with Big Data

Obtaining a word count from a big-text data source

Obtaining a sorted word count from a big-text source

Examining big-text log file access

Computing prime numbers using parallel operations

Analyzing big-text data

Analyzing big data history files

Chapter 10: Jupyter Security

How much risk?

Known vulnerabilities

Web attack strategies

Inherent Jupyter security issues

Security mechanisms built into Jupyter

Token-based authentication

Password authentication

No authentication

Using SSL

Creating an SSL certificate

Apply the SSL certificate

The Jupyter trust model

Trust overrides

Collaboration

Controlling network access

Controlling domain access

Controlling IP access

Additional practices

Server IP address

URL prefix

No browser

Chapter 11: Jupyter Labs

JupyterLab features

Installing and starting JupyterLab

Installing JupyterLab

Starting JupyterLab

JupyterLab display

JupyterLab menus

Starting a Notebook

Starting a console

Index.

Notes:

Description based on print version record.

OCLC:

1035516261