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Learning Jupyter 5 - Second Edition

Book Description

Create and share livecode, equations, visualizations, and explanatory text, in both a single document and a web browser with Jupyter

Key Features

  • Learn how to use Jupyter 5.x features such as cell tagging and attractive table styles
  • Leverage big data tools and datasets with different Python packages
  • Explore multiple-user Jupyter Notebook servers

Book Description

The Jupyter Notebook allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, and machine learning. Learning Jupyter 5 will help you get to grips with interactive computing using real-world examples.

The book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next, you will learn to integrate the Jupyter system with different programming languages such as R, Python, Java, JavaScript, and Julia, and explore various versions and packages that are compatible with the Notebook system. Moving ahead, you will master interactive widgets and namespaces and work with Jupyter in a multi-user mode.

By the end of this book, you will have used Jupyter with a big dataset and be able to apply all the functionalities you've explored throughout the book. You will also have learned all about the Jupyter Notebook and be able to start performing data transformation, numerical simulation, and data visualization.

What you will learn

  • Install and run the Jupyter Notebook system on your machine
  • Implement programming languages such as R, Python, Julia, and JavaScript with the Jupyter Notebook
  • Use interactive widgets to manipulate and visualize data in real time
  • Start sharing your Notebook with colleagues
  • Invite your colleagues to work with you on the same Notebook
  • Organize your Notebook using Jupyter namespaces
  • Access big data in Jupyter for dealing with large datasets using Spark

Who this book is for

Learning Jupyter 5 is for developers, data scientists, machine learning users, and anyone working on data analysis or data science projects across different teams. Data science professionals will also find this book useful for performing technical and scientific computing collaboratively.

Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.

Table of Contents

  1. Title Page
  2. Copyright and Credits
    1. Learning Jupyter 5 Second Edition
  3. Packt Upsell
    1. Why subscribe?
    2. PacktPub.com
  4. Contributors
    1. About the author
    2. About the reviewer
    3. Packt is searching for authors like you
  5. Preface
    1. Who this book is for
    2. What this book covers
    3. To get the most out of this book
      1. Download the example code files
      2. Conventions used
    4. Get in touch
      1. Reviews
  6. Introduction to Jupyter
    1. First look at Jupyter
    2. Installing Jupyter
    3. Notebook structure
    4. Notebook workflow
    5. Basic Notebook operations
      1. File operations
        1. Duplicate
        2. Rename
        3. Delete
        4. Upload
        5. New text file
        6. New folder
        7. New Python 3
    6. Security in Jupyter
      1. Security digest
      2. Trust options
    7. Configuration options for Jupyter
    8. Summary
  7. Jupyter Python Scripting
    1. Basic Python in Jupyter
    2. Python data access in Jupyter
    3. Python pandas in Jupyter
    4. Python graphics in Jupyter
    5. Python random numbers in Jupyter
    6. Summary
  8. Jupyter R Scripting
    1. Adding R scripting to your installation
      1. Adding R scripts to Jupyter on macOS
      2. Adding R scripts to Jupyter on Windows
      3. Adding R packages to Jupyter
      4. R limitations in Jupyter
    2. Basic R in Jupyter
    3. R dataset access
    4. R visualizations in Jupyter
      1. R 3D graphics in Jupyter
      2. R 3D scatterplot in Jupyter
    5. R cluster analysis
    6. R forecasting
    7. R machine learning
      1. Dataset
    8. Summary
  9. Jupyter Julia Scripting
    1. Adding Julia scripting to your installation
      1. Adding Julia scripts to Jupyter
      2. Adding Julia packages to Jupyter
    2. Basic Julia in Jupyter
    3. Julia limitations in Jupyter
    4. Standard Julia capabilities
    5. Julia visualizations in Jupyter
      1. Julia Gadfly scatterplot
      2. Julia Gadfly histogram
      3. Julia Winston plotting
    6. Julia Vega plotting
      1. Julia PyPlot plotting
    7. Julia parallel processing
    8. Julia control flow
    9. Julia regular expressions
    10. Julia unit testing
    11. Summary
  10. Jupyter Java Coding
    1. Adding the Java kernel to your installation
      1. Installing Java 9 or later
      2. A Jupyter environment is required
      3. Configuring IJava
        1. Downloading the IJava project from GitHub
        2. Building and installing the kernel
        3. Available options
    2. Jupyter Java console
    3. Jupyter Java output
    4. Java Optional
    5. Java compiler errors
    6. Java lambdas
    7. Java Collections
      1. Java streams
    8. Java summary statistics
    9. Summary
  11. Jupyter JavaScript Coding
    1. Adding JavaScript scripting to your installation
      1. Adding JavaScript scripts to Jupyter on macOS or Windows
    2. JavaScript Hello World Jupyter Notebook
      1. Adding JavaScript packages to Jupyter
    3. Basic JavaScript in Jupyter
    4. JavaScript limitations in Jupyter
    5. Node.js d3 package
    6. Node.js stats-analysis package
    7. Node.js JSON handling
    8. Node.js canvas package
    9. Node.js plotly package
    10. Node.js asynchronous threads
    11. Node.js decision-tree package
    12. Summary
  12. Jupyter Scala
    1. Installing the Scala kernel
    2. Scala data access in Jupyter
    3. Scala array operations
    4. Scala random numbers in Jupyter
    5. Scala closures
    6. Scala higher-order functions
    7. Scala pattern matching
    8. Scala case classes
    9. Scala immutability
    10. Scala collections
    11. Named arguments
    12. Scala traits
    13. Summary
  13. Jupyter and Big Data
    1. Apache Spark
      1. Installing Spark on macOS
      2. Windows install
    2. First Spark script
    3. Spark word count
    4. Sorted word count
    5. Estimate pi
    6. Log file examination
    7. Spark primes
    8. Spark text file analysis
    9. Spark evaluating history data
    10. Summary
  14. Interactive Widgets
    1. Installing widgets
    2. Widget basics
    3. Interact widget
      1. Interact widget slidebar
      2. Interact widget checkbox
      3. Interact widget textbox
      4. Interact dropdown
    4. Interactive widget
    5. Widgets
      1. The progress bar widget
      2. The listbox widget
      3. The text widget
      4. The button widget
      5. Widget properties
      6. Adjusting widget properties
      7. Adjusting properties
      8. Widget events
      9. Widget containers
    6. Summary
  15. Sharing and Converting Jupyter Notebooks
    1. Sharing Notebooks
      1. Sharing Notebooks on a Notebook server
        1. Sharing encrypted Notebooks on a Notebook server
      2. Sharing Notebooks on a web server
      3. Sharing Notebooks through Docker
      4. Sharing Notebooks on a public server
    2. Converting Notebooks
      1. Notebook format
      2. Scala format
      3. HTML format
      4. Markdown format
      5. Restructured text format
      6. LaTeX format
      7. PDF format
    3. Summary
  16. Multiuser Jupyter Notebooks
    1. A sample interactive Notebook
    2. JupyterHub
      1. Installation
      2. Operation
      3. Continuing with operations
      4. JupyterHub summary
    3. Docker
      1. Installation
      2. Starting Docker
      3. Building your Jupyter image for Docker
      4. Docker summary
    4. Summary
  17. What's Next?
    1. JupyterHub
    2. JupyterLab
    3. Scale
    4. Custom frontends
    5. Interactive computing standards
    6. Summary
  18. Other Books You May Enjoy
    1. Leave a review - let other readers know what you think