# Books And Videos¶

## IPython Interactive Computing and Visualization Cookbook, Second Edition¶

- IPython Interactive Computing and Visualization Cookbook, Second Edition
- By Cyrille Rossant
- 548 pages
- Packt Publishing
- January 2018

Python is one of the leading open source platforms for data science and numerical computing. IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and they constitute an ideal gateway to the platform.

IPython Interactive Computing and Visualization Cookbook, Second Edition contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code. You will apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning.

The first part of the book covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming. The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics.

## Jupyter for Data Science¶

- Jupyter for Data Science
- By Dan Toomey
- 242 pages
- Packt Publishing
- October 2017

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create documents that contain live code, equations, and visualizations. This book is a comprehensive guide to getting started with data science using the popular Jupyter notebook.

If you are familiar with Jupyter notebook and want to learn how to use its capabilities to perform various data science tasks, this is the book for you! From data exploration to visualization, this book will take you through every step of the way in implementing an effective data science pipeline using Jupyter. You will also see how you can utilize Jupyter’s features to share your documents and codes with your colleagues. The book also explains how Python 3, R, and Julia can be integrated with Jupyter for various data science tasks.

By the end of this book, you will comfortably leverage the power of Jupyter to perform various tasks in data science successfully.

## Jupyter In Depth¶

- Jupyter In Depth
- Jesse Bacon Thursday, August 31, 2017
- 1 hour and 43 minutes
- Packt Publishing
- August 2017

Jupyter has emerged as a popular tool for code exposition and the sharing of research artefacts. It has interactive display capabilities and the pluggable kernel system allows data scientists to switch back and forth between multiple programming languages.

The course will walk you through the core modules and standard capabilities of the console, client, and notebook server. By exploring the Python language, you will be able to get starter projects for configurations management, file system monitoring, and encrypted backup solutions for safeguarding their data. In the final Sections, you will be able to build dashboards in a Jupyter notebook to report back information about the project and the status of various Jupyter components.

## Jupyter Notebook for All - Part II [Video]¶

- Jupyter Notebook for All - Part II
- By Dan Toomey
- 1 hour and 14 minutes
- Packt Publishing
- March 2017

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It 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, machine learning, and much more. This tutorial 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, JavaScript, and Julia; further, you’ll explore the various versions and packages that are compatible with the Notebook system. Moving ahead, you’ll master interactive widgets, namespaces, and working with Jupyter in multiuser mode. Towards the end, you will use Jupyter with a big dataset and will apply all the functionalities learned throughout the video.

## Jupyter Notebook for All - Part I [Video]¶

- Jupyter Notebook for All - Part I
- By Dan Toomey
- 1 hour 23 minutes
- Packt Publishing
- March 2017

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It 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, machine learning, and much more. This tutorial 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, JavaScript, and Julia; further, you’ll explore the various versions and packages that are compatible with the Notebook system. Moving ahead, you’ll master interactive widgets, namespaces, and working with Jupyter in multiuser mode. Towards the end, you will use Jupyter with a big dataset and will apply all the functionalities learned throughout the video.

## Learning Jupyter¶

- Learning Jupyter
- By Dan Toomey
- 238 Pages
- Packt Publishing
- November 2016

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It 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, machine learning, and much more.

This book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next we’ll help you will learn to integrate Jupyter system with different programming languages such as R, Python, JavaScript, and Julia and explore the various versions and packages that are compatible with the Notebook system. Moving ahead, you master interactive widgets, namespaces, and working with Jupyter in a multiuser mode.

Towards the end, you will use Jupyter with a big data set and will apply all the functionalities learned throughout the book.

## Mastering IPython 4.0¶

- Mastering IPython 4.0
- by Thomas Bitterman
- 382 pages
- Packt Publishing
- May 2016
- Code available under MIT License on GitHub

This book will get IPython developers up to date with the latest advancements in IPython and dive deep into interactive computing with IPython. This an advanced guide on interactive and parallel computing with IPython will explore advanced visualizations and high-performance computing with IPython in detail.

You will quickly brush up your knowledge of IPython kernels and wrapper kernels, then we’ll move to advanced concepts such as testing, Sphinx, JS events, interactive work, and the ZMQ cluster. The book will cover topics such as IPython Console Lexer, advanced configuration, and third-party tools.

By the end of this book, you will be able to use IPython for interactive and parallel computing in a high-performance computing environment.

## IPython Cookbook¶

- IPython Interactive Computing and Visualization Cookbook
- by Cyrille Rossant
- 512 pages
- Packt Publishing
- September 25 2014

This is an advanced-level guide to IPython for data science, and the sequel of the IPython minibook.

## IPython Minibook¶

- Learning IPython for Interactive Computing and Data Visualization
- by Cyrille Rossant
- 175 pages
- Packt Publishing
- October 25 2015

This book is a beginner-level introduction to Python for data analysis, covering IPython, the Jupyter Notebook, pandas, NumPy, matplotlib, and many other libraries. There is an introduction to the Python programming language for complete beginners. There are also contents for more advanced users, like parallel computing with IPython and high-performance computing with Numba and Cython.

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