Book Image

Jupyter Cookbook

By : Dan Toomey
Book Image

Jupyter Cookbook

By: Dan 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.

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Table of Contents (17 chapters)
Title Page
Title Page
Copyright and Credits
Copyright and Credits
Packt Upsell
Packt Upsell
Contributors
Contributors
Preface
Preface
Free Chapter
1
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
2
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
3
Accessing and Retrieving Data
Accessing and Retrieving Data
Introduction
Reading CSV files
Reading JSON files
Accessing a database
Reading flat files
Reading text files
4
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
5
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
6
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
7
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
8
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
9
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
10
Jupyter Security
Jupyter Security
Introduction
Security mechanisms built into Jupyter
Using SSL
The Jupyter trust model
Controlling network access
Additional practices
11
Jupyter Labs
Jupyter Labs
Introduction
Installing and starting JupyterLab
JupyterLab display
JupyterLab menus
Starting a Notebook
Starting a console
Index
Index

Visualizing with Julia

Julia is a programming language specifically built for numerical computing. There are several features that make it a great fit for use as a Jupyter scripting language. We will use several of the available packages for visualization.

Of special note is that Julia has direct access to most R packages, so those can be used as well.

Note

A word of caution: I could not produce most Julia visualizations on a Windows PC. For this section, I used a Mac, and even then I had to try adding packages several times before things started to work.

Getting ready

Before using Julia scripts, you should add some standard packages and update all packages to the current levels. You can do that with these commands directly in the Julia console rather than in a Notebook:

Note

I would recommend that you add a package by closing the Notebook, opening a Julia console, adding the package, reopening the Notebook, and then using the new package.

Pkg.add("DataFrames")
Pkg.add("RDatasets")
Pkg.add("Gadfly...
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