F.K: To my coauthor Paul for all your creative ideas and dedication. It was truly a pleasure working with you. To Jeanne, Stephanie, Matthew, and Conor, for all your love and support.
P.K.: To my coauthor F.K., for your persistence and thoughtful discussion. To Ryan, for your support and distraction when I needed a break.
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Introduction to Python Programming for Business and Social Science Applications
Frederick Kaefer
Loyola University Chicago
Paul Kaefer
Carrot Health
Copyright © 2021 by SAGE Publications, Inc.
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Library of Congress Cataloging-in-Publication Data
Names: Kaefer, Frederick, author. | Kaefer, Paul, author.
Title: Introduction to Python programming for business and social science applications / Frederick Kaefer, Loyola University Chicago, Paul Kaefer, Carrot Health.
Description: First Edition. | Thousand Oaks : SAGE Publications, Inc, 2020. | Includes bibliographical references.
Identifiers: LCCN 2020021883 | ISBN 9781544377445 (paperback) | ISBN 9781544377452 (epub) | ISBN 9781544377483 (epub) | ISBN 9781544377476 (ebook)
Subjects: LCSH: Business enterprises–Technological innovations. | Business enterprises–Computer programs. | Python (Computer program language)
Classification: LCC HD45 .K234 2020 | DDC 005.13/3–dc23
LC record available at https://lccn.loc.gov/2020021883
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Preface
Why Python?
This textbook focuses on the Python programming language, but some readers might ask, “Why use Python instead of more established statistical packages such as IBM® SPSS® (Statistical Package for the Social Sciences) and SAS® (Statistical Analysis System) or other programming languages such as R?” Several advantages that Python has compared to SPSS and SAS are that Python is open source and can run on many platforms. Users are free to make copies, distribute, and even change the software. Like Python, the R programming language is also an open-source programming language used for data analytics. There is prolific use of both R and Python in the business world and for academic and research purposes. We focus on Python because we see a need for a text that presents Python programming specifically for those in the fields of social sciences and business to develop applications for data analytics. Whereas some may prefer R for statistical analysis and plotting charts, Python is a general-purpose scripting language used to develop applications with graphical user interfaces (GUIs), and many favor Python when working with text-based data. While there are possibilities of developing GUI applications using R, it is cumbersome to develop applications using R the way we approach them in this book. In our experience, covering the development of GUIs using the Python tkinter package (Chapter 11) right after control logic (Chapter 4) has piqued the interest of students through the creation of interactive, user-friendly applications. Although working with the Python tkinter package has some challenges,
