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Jupyter Cookbook

By : Toomey

1 (1)

Jupyter Cookbook

1 (1)

By: Toomey

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.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

Installation and Setting up the Environment

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

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

Accessing and Retrieving Data

Introduction

Reading CSV files

Reading JSON files

Accessing a database

Reading flat files

Reading text files

Visualizing Your Analytics

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

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

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

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

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

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

Jupyter Security

Introduction

Security mechanisms built into Jupyter

Using SSL

The Jupyter trust model

Controlling network access

Additional practices

Jupyter Labs

Jupyter Labs

Introduction

Installing and starting JupyterLab

JupyterLab display

JupyterLab menus

Starting a Notebook

Starting a console

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Converting Notebooks to Markdown

The Markdown (files with .md extension) format is a looser version of HTML, which is another Markdown format. The README files of many software distribution packages are stored in Markdown format.

How to do it...

Once we have a Notebook in use in Jupyter, we can select the Download Markdown menu choice. As with others, Jupyter prompts you for the location to store the file.

How it works...

The downloaded Markdown file is named with the name of your Notebook followed by the file extension .zip, as there are multiple components to the Markdown. Using the same example, my download was named B09656_07+r+iris+for+conversions.zip. In my .zip file, there were:

B09656_07 r iris for conversions.md: The Markdown file
output_2_0.png: The corresponding graphic

If we use an md file viewer (there are many available), we get a familiar-looking screenshot:

And the corresponding graphic is an exact match.

Looking inside of the .md file, we see a lot of HTML tags used for the table...

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