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Mastering pandas for Finance
Master pandas, an open source Python Data
Analysis Library, for financial data analysis
Michael Heydt
BIRMINGHAM - MUMBAI
Mastering pandas for Finance
Copyright © 2015 Packt Publishing
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First published: May 2015
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ISBN 978-1-78398-510-4
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Credits
Author Project Coordinator
Michael Heydt Neha Bhatnagar
Reviewers Proofreaders
James Beveridge Stephen Copestake
Philipp Deutsch Safis Editing
Jon Gaither
Jim Holmström Indexer
Mariammal Chettiyar
Francesco Pochetti
Graphics
Commissioning Editor
Sheetal Aute
Kartikey Pandey
Disha Haria
Content Development Editor
Merwyn D'souza Production Coordinator
Conidon Miranda
Technical Editor
Shashank Desai Cover Work
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Copy Editor
Sarang Chari
About the Author
Michael Heydt is an independent consultant, educator, and trainer with nearly
30 years of professional software development experience, during which time, he
focused on Agile software design and implementation using advanced technologies
in multiple verticals, including media, finance, energy, and healthcare. He holds
an MS degree in mathematics and computer science from Drexel University and
an executive master's of technology management degree from the University of
Pennsylvania's School of Engineering and Wharton Business School. His studies
and research have focused on technology management, software engineering,
entrepreneurship, information retrieval, data sciences, and computational finance.
Since 2005, he has specialized in building energy and financial trading systems
for major investment banks on Wall Street and for several global energy-trading
companies, utilizing .NET, C#, WPF, TPL, DataFlow, Python, R, Mono, iOS,
and Android. His current interests include creating seamless applications using
desktop, mobile, and wearable technologies, which utilize high-concurrency,
high-availability, and real-time data analytics; augmented and virtual reality; cloud
services; messaging; computer vision; natural user interfaces; and software-defined
networks. He is the author of numerous technology articles, papers, and books. He
is a frequent speaker at .NET user groups and various mobile and cloud conferences,
and he regularly delivers webinars and conducts training courses on emerging
and advanced technologies. To know more about Michael, visit his website at
http://bseamless.com/.
About the Reviewers
James Beveridge is a product analyst and machine learning specialist. He earned
his BS degree in mathematics from Cal Poly, San Luis Obispo, CA. He has worked
with the finance and analytics teams in technology and marketing companies in
the Bay Area, Chicago, and New York. His current work focuses on segmentation
and classification modeling, statistics, and product development. He has enjoyed
contributing to this book as a technical reviewer.
Philipp Deutsch obtained degrees in mathematics and physics from the University
of Vienna and the Vienna University of Technology before starting a career in financial
services and consulting. He has worked on a number of projects involving data
analytics across Europe, both in the banking and consumer/retail sectors, and has
extensive experience in Python, R, and SQL. He currently lives in London.
Jon Gaither is a senior information systems student at Clemson University with
a background in finance. He started learning Python during his sophomore year of
college. Since then, he has dabbled in frameworks such as Flask, Django, and pandas
purely out of interest. Outside of Python, Jon has studied Java, SAS, VBA, and SQL.
His professional experience comes from internships in financial services and satellite
communications.
Jim Holmström is soon to graduate with a bachelor's degree in engineering
physics and a master's degree in machine learning from KTH Royal Institute of
Technology, Stockholm.
He is currently a developer and partner at Watty—an electricity data analysis
start-up that creates a breakdown of a household's energy spending from the total
electricity consumption data. Watty's leading-edge technology stack has pandas as
an integral part.
Both professionally and in his free time, he enjoys data analysis, functional
programming, and well-structured code.
For more information, visit http://portfolio.jim.pm.
Francesco Pochetti graduated in physical chemistry in Rome in 2012 and was
employed at Avio in Italy. He worked there for 2 years as a solid rocket propellant
specialist, taking care of the formulation and development of rocket fuels for both
military and aerospace purposes. In July 2014, he moved to Berlin to attend Data
Science Retreat—a 3-month boot camp in data analysis and machine learning in
Python and R. After this short German experience, he ended up at Amazon in
Luxembourg, where he currently works as a business analyst for Kindle content.
In his spare time, he likes to read and play around with several programming
languages, Python being among his preferred ones. You can follow him and his
data-related projects at http://francescopochetti.com/.
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Table of Contents
Preface v
Chapter 1: Getting Started with pandas Using Wakari.io 1
What is Wakari? 2
Creating a Wakari cloud account 3
Updating existing packages 6
Installing new packages 7
Installing the samples in Wakari 10
Summary 12
Chapter 2: Introducing the Series and DataFrame 13
Notebook setup 14
The main pandas data structures – Series and DataFrame 14
The Series 14
The DataFrame 15
The basics of the Series and DataFrame objects 15
Creating a Series and accessing elements 16
Size, shape, uniqueness, and counts of values 19
Alignment via index labels 21
Creating a DataFrame 23
Example data 26
Selecting columns of a DataFrame 27
Selecting rows of a DataFrame using the index 30
Slicing using the [] operator 31
Selecting rows by the index label and location – .loc[] and .iloc[] 32
Selecting rows by the index label and/or location – .ix[] 33
Scalar lookup by label or location using .at[] and .iat[] 34
Selecting rows using the Boolean selection 35
Arithmetic on a DataFrame 36
Reindexing the Series and DataFrame objects 39
Summary 44
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