href="#uce5025d3-9fef-5faa-9444-d5470faad52a">Chapter 6, we use taxi trip data to illustrate features from the NumPy and Pandas packages, and a data set from the GSS is used to illustrate data cleaning and preparation using the Pandas package. In Chapter 7, the BeautifulSoup package illustrates how it is not always as easy as one might expect to obtain data by web scraping them from a web page (using the GSS website). In Chapter 7, we also use REST API queries to obtain data from the taxi trips data set directly from the Chicago Data Portal website. In Chapter 8, variables from both the GSS and taxi trips data sets illustrate statistical analysis. In Chapter 9, data in both data sets demonstrate how the matplotlib package visualizes data. In Chapter 10, both the GSS data set and the taxi trip data set illustrate different machine learning classification techniques. In Chapter 11, we develop a graphical user interface using the tkinter package with data from the taxi trips data set. Two tables that more carefully detail the examples presented by data set throughout the textbook immediately follow this preface.
Digital Resources
Visit study.sagepub.com/researchmethods/statistics/kaefer-intro-to-python for downloadable study resources to accompany this text. Resources include Python code files, data sets, and Stop, Code and Understand! exercises and solutions.
Figures and Tables in the Text Related to the GSS Data Set
Figures and Tables in the Text Related to the Taxi Trips Data Set
Python Modules and Packages
Acknowledgments
Thank you to Leah Fargotstein, Acquisitions Editor—Research Methods, Statistics, and Evaluation at SAGE Publishing, for all your assistance throughout the development of this book.
Thank you to undergraduate and master’s-level business students at Loyola University Chicago for your feedback on earlier drafts of this book.
Thank you to the following reviewers for all your suggestions that helped improve this book.
Jean Mark Gawron, San Diego State University
Charles J. Gomez, City University of New York, Queens College
David Han, The University of Texas at San Antonio
Lenwood S. Heath, Virginia Tech
Gabe Ignatow, University of North Texas
Hakan Islamoglu, Recep Tayyip Erdogan University
Patrick Christian Kaminski, Indiana University Bloomington
Jacqueline Masloff, Bentley University
Neba Nfonsang, University of Denver
James O’Brien, Pennsylvania State University
D. Dwayne Paschall, University of Dallas
Benjamin Soltoff, University of Chicago
Ryan Sougstad, University of Minnesota
Damian Trilling, University of Amsterdam
Giovanni Vincenti, University of Baltimore
Wei Wang, Graduate Center, the City University of New York
Chong Ho Yu, Azusa Pacific University
About the Authors
Frederick Kaeferis Associate Professor of Information Systems at the Loyola University Chicago Quinlan School of Business. After completing a bachelor’s degree in Mathematics and Computer Science, he worked as a mainframe programmer for several years before earning an MBA with concentrations in Finance and Information Systems and a PhD in Management Information Systems. Professor Kaefer has taught computer programming and other information systems courses to business students for over 25 years. In addition to his interest in the Python programming language, Professor Kaefer has taught courses including Data Structures Using C and VBA Programming in MS Office.Paul Kaeferworks as Senior Analytics Engineer at Carrot Health and has instructed two data analytics and visualization bootcamps through Trilogy Education Services. He previously worked for UnitedHealthcare as a data scientist. After earning a bachelor’s degree in Computer Engineering, he earned a master’s degree in Computational Sciences while leading the Data Analysis Team for the GasDay project, a research lab at Marquette University that works with energy utilities around the United States to forecast natural gas demand. In addition to his interest in the Python programming language, Paul has certifications in the SAS programming and R programming languages and is building experience using Tableau.
1 Introduction to Python
Learning Objectives
Explain Python’s background and important features
Describe free, open-source software (FOSS)
Summarize Python’s user community and available resources
Install Python’s platform-independent interpreter
Execute Python code in an Interactive Development Environment (IDE)
Describe the two data sets used throughout the book
Introduction
This chapter gives a brief background of Python and then goes on to illustrate Python programming using an Interactive Development Environment (IDE). Python is an interpreted computer programming language in which you can enter code instructions one at a time or as part of a larger program, which comprises many instructions. Throughout this book, illustrations of entering and executing Python code provide hands-on experience and familiarity with programming in Python. The Python code examples begin in this chapter with writing and running a sample instruction of Python code that prints a simple message to the screen. At the end of the chapter, we introduce the two real-world, large-scale data sets that we will use throughout the book. These data sets embody many different types of data and are well suited for the data analysis and visualization covered in later
