Books

Learning Jupyter

Learning Jupyter

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

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 Cookbook

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

IPython Minibook

IPython Minibook

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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