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Jupyter Cookbook

Jupyter Cookbook

By : Toomey
1 (1)
Buy this Book
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Jupyter Cookbook

Jupyter Cookbook

1 (1)
By: 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.
Table of Contents (12 chapters)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Get in touch
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1
Installation and Setting up the Environment
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Installation and Setting up the Environment
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Introduction
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Installing Jupyter on Windows
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Installing Jupyter on the Mac
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Installing Jupyter on Linux
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Installing Jupyter on a server
2
Adding an Engine
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Adding an Engine
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Introduction
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Adding the Python 3 engine
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Adding the R engine
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Adding the Julia engine
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Adding the JavaScript engine
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Adding the Scala engine
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Adding the Spark engine
3
Accessing and Retrieving Data
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Accessing and Retrieving Data
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Introduction
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Reading CSV files
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Reading JSON files
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Accessing a database
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Reading flat files
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Reading text files
4
Visualizing Your Analytics
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Visualizing Your Analytics
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Introduction
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Generating a line graph using Python
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Generating a histogram using Python
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Generating a density map using Python
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Plotting 3D data using Python
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Present a user-interactive graphic using Python
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Visualizing with R
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Generate a regression line of data using R
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Generate an R lowess line graph
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Producing a Scatter plot matrix using R
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Producing a bar chart using R
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Producing a word cloud using R
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Visualizing with Julia
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Drawing a Julia scatter diagram of Iris data using Gadfly
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Drawing a Julia histogram using Gadfly
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Drawing a Julia line graph using the Winston package
5
Working with Widgets
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Working with Widgets
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Introduction
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What are widgets?
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Using ipyleaflet widgets
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Using ipywidgets
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Using a widget container
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Using an interactive widget
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Using an interactive text widget
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Linking widgets together
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Another ipywidgets linking example
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Using a cookie cutter widget
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Developing an OPENGL widget
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Creating a simple orbit of one object
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Using a complex orbit of multiple objects
6
Jupyter Dashboards
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Jupyter Dashboards
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Introduction
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What is Jupyter dashboards?
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Creating an R dashboard
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Create a Python dashboard
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Creating a Julia dashboard
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Develop a JavaScript (Node.js) dashboard
7
Sharing Your Code
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Sharing Your Code
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Introduction
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Using a Notebook server
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Using a web server
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Sharing your Notebook through a public server
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Sharing your Notebook through Docker
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Sharing your Notebook using nbviewer
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Converting your Notebook into a different format
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Converting Notebooks to R
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Converting Notebooks to HTML
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Converting Notebooks to Markdown
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Converting Notebooks to reStructedText
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Converting Notebooks to Latex
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Converting Notebooks to PDF
8
Multiuser Jupyter
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Multiuser Jupyter
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Introduction
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Why multiuser?
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Providing multiuser with JupyterHub
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Providing multiuser with Docker
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Running your Notebook in Google Cloud Platform
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Running your Notebook in AWS
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Running your Notebook in Azure
9
Interacting with Big Data
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Interacting with Big Data
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Introduction
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Obtaining a word count from a big-text data source
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Obtaining a sorted word count from a big-text source
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Examining big-text log file access
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Computing prime numbers using parallel operations
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Analyzing big-text data
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Analyzing big data history files
10
Jupyter Security
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Jupyter Security
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Introduction
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Security mechanisms built into Jupyter
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Using SSL
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The Jupyter trust model
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Controlling network access
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Additional practices
11
Jupyter Labs
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Jupyter Labs
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Introduction
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Installing and starting JupyterLab
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JupyterLab display
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JupyterLab menus
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Starting a Notebook
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Starting a console

Using an interactive widget

interactive is another set of widgets in the ipywidget library. They are specifically built to expect to call upon a handler when the value changes.

How to do it...

We can use this script:

def mycheckfunction(x):
    print(x)
    return x

interactive_checkbox = interactive(mycheckfunction, x=False)
interactive_checkbox

This results in this display:

Here, as we click on the checkbox, the value of the box changes and the corresponding value is printed in the display.

How it works...

The constructor for the interactive widget has the first argument as the name of the handler to use when the value changes. The second argument is the default value. In this case, we have a checkbox, so the value is False. It could be True as well.

When you click on the box the value changes, triggering a call to the handler. The handler prints out the current value of the box.

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