Python Data Analysis - Second Edition

Armando Fandango

• Publisher: Packt Publishing
• Year: 2017
• eBook ISBN: 9781787127920
• Edition: 2
• Topic: Computer Science
• Subtopic: Data Modelling & Design
• Index: Computer Science

Learn how to apply powerful data analysis techniques with popular open source Python modulesAbout This Book• Find, manipulate, and analyze your data using the Python 3.5 libraries• Perform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python code• An easy-to-follow guide with realistic examples that are frequently used in real-world data analysis projects.Who This Book Is ForThis book is for programmers, scientists, and engineers who have the knowledge of Python and know the basics of data science. It is for those who wish to learn different data analysis methods using Python 3.5 and its libraries. This book contains all the basic ingredients you need to become an expert data analyst.What You Will Learn• Install open source Python modules such NumPy, SciPy, Pandas, stasmodels, scikit-learn, theano, keras, and tensorflow on various platforms• Prepare and clean your data, and use it for exploratory analysis• Manipulate your data with Pandas• Retrieve and store your data from RDBMS, NoSQL, and distributed filesystems such as HDFS and HDF5• Visualize your data with open source libraries such as matplotlib, bokeh, and plotly• Learn about various machine learning methods such as supervised, unsupervised, probabilistic, and Bayesian• Understand signal processing and time series data analysis• Get to grips with graph processing and social network analysisIn DetailData analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks.With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis.The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.Style and approachThe book takes a very comprehensive approach to enhance your understanding of data analysis. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work. Packed with clear, easy to follow examples, this book will turn you into an ace data analyst in no time.

Python Data Analysis - Second Edition

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Python Data Analysis - Second Edition

Copyright © 2017 Packt Publishing

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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.

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First published: March 2017

Production reference: 1230317

Published by Packt Publishing Ltd.

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B3 2PB, UK.

ISBN 978-1-78712-748-7

www.packtpub.com

Credits

Author Armando Fandango	Copy Editor Safis Editing
Reviewers Joran Beasley Ratan Kumar	Project Coordinator Shweta H Birwatkar
Commissioning Editor Amey Varangoankar	Proofreader Safis Editing
Acquisition Editor Tushar Gupta	Indexer Aishwarya Gangawane
Content Development Editor Amrita Noronha	Graphics Tania Dutta
Technical Editor Deepti Tuscano	Production Coordinator Arvindkumar Gupta

About the Author

Armando Fandango is Chief Data Scientist at Epic Engineering and Consulting Group, and works on confidential projects related to defense and government agencies. Armando is an accomplished technologist with hands-on capabilities and senior executive-level experience with startups and large companies globally. His work spans diverse industries including FinTech, stock exchanges, banking, bioinformatics, genomics, AdTech, infrastructure, transportation, energy, human resources, and entertainment.

Armando has worked for more than ten years in projects involving predictive analytics, data science, machine learning, big data, product engineering, high performance computing, and cloud infrastructures. His research interests spans machine learning, deep learning, and scientific computing.

I would like to thank my wife for supporting me while I was writing this book. I would like to thank Dr. Paul Wiegand at UCF for always inspiring me to pursue great opportunities. I am highly indebted to the team at Packt: Tushar, Sumeet, Amrita, Deepti, and many others who made this work possible for the readers.

About the Reviewers

Joran Beasley received his degree in computer science from the University of Idaho. He has been programming desktop applications in Python professionally for monitoring large-scale sensor networks for use in agriculture for the last 7 years. He currently lives in Moscow, Idaho, and works at METER Group. as a software engineer.

I would like to thank my wife, Nicole, for putting up with my long hours hunched over a keyboard, and her constant support and help in raising our two wonderful children.

Ratan Kumar has been programming software in various languages and technologies for the past 4 years. Having used Python in the fields of web services for personal as well as professional projects since 2013, he finds it to be one of the most elegant, productive, and easy to pick up programming languages. Ratan is currently based in Bangalore, where he is part of the core team at smallcase, which simplifies stock market investments.

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Preface

Data analysis has a rich history in natural, biomedical, and social sciences. In almost every area of industry, data analysis has gained popularity lately due to the hype around Data Science. Data analysis and Data Science attempt to extract information from data. For that purpose, we use techniques from statistics, machine learning, signal processing, natural language processing, and computer science.

A mind map visualizing Python software that can be used for data analysis can be found in first chapter of this book. The first noticeable thing is that the Python ecosystem is very mature, diverse and rich. It includes famous packages such as NumPy, SciPy, and matplotlib. This should not come as a surprise since Python has been around since 1989. Python is easy to learn and use, less verbose than other programming languages, and very readable. Even if you don't know Python, you can pick up the basics within days, especially if you have experience in another programming language. To enjoy this book, you don't need more than the basics. There are plenty of books, courses, and online tutorials that teach Python.

What this book covers

Chapter 1, Getting Started with Python Libraries, gives instructions to install python and fundamental python data analysis libraries. We create a small application using NumPy and draw some basic plots with matplotlib.

Chapter 2, NumPy Arrays, introduces us to NumPy fundamentals and arrays. By the end of this chapter, we will have basic understanding of NumPy arrays and the associated functions.

Chapter 3, T...

Table of contents

• Python Data Analysis - Second Edition