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

Adding the Python 3 engine

Jupyter was originally derived from Python with the IPython project. At that time, Python 2 was the predominant version available for use. For every installation of Jupyter, the default engine provided is Python 2. There are a number of changes involved when moving from Python 2 to Python 3, especially in the underlying libraries that you may be invoking, where parameter and usage changes have occurred.

How to do it...

We will cover the installation of the Python 3 engine and make sure it is running with a script.

Installing the Python 3 engine

Assuming you have installed the standard Jupyter package, you now have Python 2 as the only engine available at the top of the Jupyter portal screen:

To upgrade to Python 3, I used the commands:

conda create -n py3k python=3 
anaconda source activate py3k 
ipython kernelspec install-self

After this, when you start Jupyter, you will have the Python 3 engine choice.

You may prefer to have the Python 2 engine also available. This could...

Jupyter Cookbook

By : Dan Toomey

Jupyter Cookbook

By: Dan Toomey

Overview of this book

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