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Mastering Python Data Analysis
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Mastering Python Data Analysis
Copyright © 2016 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, and its dealers and distributors will be held liable for any damages caused or alleged to be 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.
Publishing Month: June 2016
Production reference: 1230616
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.
ISBN 978-1-78355-329-7
www.packtpub.com
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Magnus Vilhelm Persson is a scientist with a passion for Python and open source software usage and development. He obtained his PhD in Physics/Astronomy from Copenhagen University’s Centre for Star and Planet Formation (StarPlan) in 2013. Since then, he has continued his research in Astronomy at various academic institutes across Europe. In his research, he uses various types of data and analysis to gain insights into how stars are formed. He has participated in radio shows about Astronomy and also organized workshops and intensive courses about the use of Python for data analysis.
You can check out his web page at http://vilhelm.nu.
This book would not have been possible without the great work that all the people at Packt are doing. I would like to highlight Arun, Bharat, Vinay, and Pranil's work. Thank you for your patience during the whole process. Furthermore, I would like to thank Packt for giving me the opportunity to develop and write this book, it was really fun and I learned a lot. There where times when the work was little overwhelming, but at those times, my colleague and friend Alan Heays always had some supporting words to say. Finally, my wife, Mihaela, is the most supportive partner anyone could ever have. For all the late evenings and nights where you pushed me to continue working on this to finish it, thank you. You are the most loving wife and best friend anyone could ever ask for.
Luiz Felipe Martins holds a PhD in applied mathematics from Brown University and has worked as a researcher and educator for more than 20 years. His research is mainly in the field of applied probability. He has been involved in developing code for open source homework system, WeBWorK, where he wrote a library for the visualization of systems of differential equations. He was supported by an NSF grant for this project. Currently, he is an associate professor in the department of mathematics at Cleveland State University, Cleveland, Ohio, where he has developed several courses in applied mathematics and scientific computing. His current duties include coordinating all first-year calculus sessions.
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Hang (Harvey) Yu is a data scientist in Silicon Valley. He works on search engine development and model optimization. He has ample experience in big data and machine learning. He graduated from the University of Illinois at Urbana-Champaign with a background in data mining and statistics. Besides this book, he has also reviewed multiple other books and papers including
Mastering Python Data Visualization and
R Data Analysis Cookbook both by Packt Publishing. When Harvey is not coding, he is playing soccer, reading fiction books, or listening to classical music. You can get in touch with him at
[email protected] or on LinkedIn at
http://www.linkedin.com/in/hangyu1.
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The use of Python for data analysis and visualization has only increased in popularity in the last few years. One reason for this is the availability and continued development of a number of excellent tools for conducting advanced data analysis and visualization. Another reason is the possibility of rapid and easy development, deployment, and sharing of code. For these reasons, Python has become one of the most widely used programming and scripting language for data analysis in many industries.
The aim of this book is to develop skills to effectively approach almost any data analysis problem, and extract all of the available information. This is done by introducing a range of varying techniques and methods such as uni- and multi-variate linear regression, cluster finding, Bayesian analysis, machine learning, and time series analysis. Exploratory data analysis is a key aspect to get a sense of what can be done and to maximize the insights that are gained from the data. Additionally, emphasis is put on presentation-ready figures that are clear and easy to interpret.
Knowing how to explore data and present results and conclusions from data analysis in a meaningful way is an important skill. While the theory behind statistical analysis is important to know, to be able to quickly and accurately perform hands-on sorting, reduction, analysis, and subsequently present the insights gained, is a make or break for today's quickly evolving business and academic sector.
Chapter 1, Tools of the Trade, provides an overview of the tools available for data analysis in Python and details the packages and libraries that will be used i...
