Jupyter Cookbook

Jupyter Cookbook

By : Dan Toomey

Buy this Book

Jupyter Cookbook

By: Dan Toomey

Buy this Book

Overview of this book

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.

Title Page

Packt Upsell

Contributors

Preface

Free Chapter

Installation and Setting up the Environment

Introduction

Installing Jupyter on Windows

Installing Jupyter on the Mac

Installing Jupyter on Linux

Installing Jupyter on a server

Adding an Engine

Introduction

Adding the Python 3 engine

Adding the R engine

Adding the Julia engine

Adding the JavaScript engine

Adding the Scala engine

Adding the Spark engine

Accessing and Retrieving Data

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

Working with Widgets

Introduction

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

Developing an OPENGL widget

Creating a simple orbit of one object

Using a complex orbit of multiple objects

Jupyter Dashboards

Introduction

What is Jupyter dashboards?

Creating an R dashboard

Create a Python dashboard

Creating a Julia dashboard

Develop a JavaScript (Node.js) dashboard

Sharing Your Code

Introduction

Using a Notebook server

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

Multiuser Jupyter

Introduction

Why multiuser?

Providing multiuser with JupyterHub

Providing multiuser with Docker

Running your Notebook in Google Cloud Platform

Running your Notebook in AWS

Running your Notebook in Azure

Interacting with Big Data

Introduction

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

Jupyter Security

Introduction

Security mechanisms built into Jupyter

Using SSL

The Jupyter trust model

Controlling network access

Additional practices

Jupyter Labs

Introduction

Installing and starting JupyterLab

Index

Customer Reviews

5 star

4 star

3 star

2 star

1 star

Installing Jupyter on the Mac

Apple Macintosh provides a graphical interface that runs on the OS/X operating system. OS/X includes running BSD under the hood. BSD is a version of Unix originally developed at Berkeley. As a version of Unix, it has all the standard developer tools expected by Jupyter to install and upgrade the software; they are built-in tools.

Getting ready

Jupyter can be installed on the Mac using Anaconda (as before for Windows) or via the command line.

How to do it...

In this section, we will go through the steps for installing Jupyter on Mac.

Installing Jupyter on the Mac via Anaconda

Just as with Windows earlier, we download the latest version of Anaconda and run the installation program. One of the screens should look like this:

The Anaconda install is very typical for Mac installs: users can run the program and make sure they want to allocate so much storage for the application to install.

Once installed, Jupyter (and Anaconda Navigator) is available just like any other application on the system. You can run Jupyter directly, or you can launch Jupyter from the Anaconda Navigator display.

Installing Jupyter on the the Mac via the command line

Many Mac users will prefer using the command line to install Jupyter. Using the command line, you can decide whether to install Jupyter with the Python 2 or Python 3 engine. If you want to add the Python 2 engine as a choice in Jupyter, you can follow similar steps for doing so in the earlier Windows command line installation section.

The script to install Jupyter on Mac via the command line with the Python 3 engine is:

bash ~/Downloads/Anaconda3-5.0.0-MacOSX-x86_64.sh

Similarly, the command to install with the Python 2 engine is as follows:

bash ~/Downloads/Anaconda2-5.0.0-MacOSX-x86_64.sh

In either case, you will be prompted by some regular install questions:

Review and agree to the license agreement
Specify whether the standard install directory is OK (if not, you can specify where to install the software)
Specify whether to prepend the Anaconda location in your user path (Anaconda recommends this step)

At this point, you should be able to start Jupyter with the command line and see the appropriate engine choice available:

jupyter notebook

Jupyter Cookbook

By : Dan Toomey

Jupyter Cookbook

By: Dan Toomey

Overview of this book

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