Dan Toomey - Jupyter Cookbook: Over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more
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Leverage the power of the popular Jupyter notebooks to simplify your data science tasks without any hassle
About This Book- Create and share interactive documents with live code, text and visualizations
- Integrate popular programming languages such as Python, R, Julia, Scala with Jupyter
- Develop your widgets and interactive dashboards with these innovative recipes
This cookbook is for data science professionals, developers, technical data analysts, and programmers who want to execute technical coding, visualize output, and do scientific computing in one tool. Prior understanding of data science concepts will be helpful, but not mandatory, to use this book.
What You Will Learn- Install Jupyter and configure engines for Python, R, Scala and more
- Access and retrieve data on Jupyter Notebooks
- Create interactive visualizations and dashboards for different scenarios
- Convert and share your dynamic codes using HTML, JavaScript, Docker, and more
- Create custom user data interactions using various Jupyter widgets
- Manage user authentication and file permissions
- Interact with Big Data to perform numerical computing and statistical modeling
- Get familiar with Jupyters next-gen user interface - JupyterLab
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.
Style and approachThe recipes in this book are highly practical and very easy to follow, and include tips and tricks that will help you crack any problem that you might come across while getting the most out of your Jupyter notebook.
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.
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Copyright 2018 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Commissioning Editor: Pravin Dhandre
Acquisition Editor: Tushar Gupta
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Technical Editor: Sagar Sawant
Copy Editor: Vikrant Phadke, Safis Editing
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Graphics: Tania Dutta
Production Coordinator: Arvindkumar Gupta
First published: April 2018
Production reference: 1270418
Published by Packt Publishing Ltd.
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ISBN 978-1-78883-944-0
www.packtpub.com
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Dan Toomey has been developing applications for over 20 years. He has worked in a variety of industries and companies of all sizes, in roles ranging from sole contributor to the VP/CTO level. For the last 10 years or so, he has been contracting with companies in the eastern Massachusetts area, under Dan Toomey Software Corp. Dan has also written a few books with Packt Publishing.
Nikhil Borkar holds the CQF designation and a PG degree in Quantitative Finance from the University of Mumbai. He works as an independent fintech and legal consultant. Prior to this, he was with Morgan Stanley Capital International as a Global RFP Project Manager. He has worked on Quantitative Finance and economic research projects using R, Python, and Excel VBA. He loves to approach problems in a multidisciplinary, holistic way. He is actively working on machine learning, AI, and deep learning projects.
Nikhil Akki started his career in IT support, and within a couple of years shifted to sales and marketing (after pursing MBA). Nikhil works at Deloitte in Mumbai as data science consultant. He has hands-on experience in building enterprise-grade - NLP, machine learning, and recommender system-based applications for his clients. He is often found taking up MOOCs and Kaggle problems. Prior to his current role, he taught Business Statistics at postgraduate level in an upcoming B-School in South Mumbai.
Juan Toms Oliva Ramos is an environmental engineer from the University of Guanajuato, Mexico, with a master's degree in administrative engineering and quality. He has over five years of experience in management and development of patents, technological innovation projects, and technological solutions through statistical control of processes. He has been a teacher of statistics, entrepreneurship, and technological development since 2011. He has developed prototypes via programming and automation technologies for improvement of operations, which have been registered for patents.
I want to thank my wife, Brenda, our two magic princesses (Maria Regina and Maria Renata) and Angel Tadeo), All of you are my strengths, happiness and my desire to look for the best for you.
, Installation and Setting up the Environment , teaches you how to install Jupyter on different environments, such as Windows, macOS, Linux, and a server machine.
, Adding an Engine , shows you the steps to add these engines to your Jupyter installation so that you can script your Notebook in the language you like.
, Accessing and Retrieving Data , teaches how to access and retrieve data from files in different formats in Jupyter.
, Visualize Your Analytics , covers recipes for visualizing your analytics in Python, R, and Julia.
, Working with Widgets , describes the wide range of possibilities of widgets in Jupyter.
, Jupyter Dashboards , teaches how to install and enable Jupyter dashboards layout extension to your Notebook.
, Sharing Your Code , shows you several methods for sharing your Notebook with others, including using different software packages and converting the Notebook into a different form for readers without access to Jupyter.
, Multiuser Jupyter , explores several options for enabling Jupyter Notebooks as a multiuser platform.
, Interacting with Big Data , covers the methods of accessing big data from Jupyter.
, Jupyter Security , investigates the various security mechanisms available for your Jupyter Notebook.
, Jupyter Labs , lets us try new features of Jupyter in a lab environment to create our own sample Notebook.
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