Information
Professionals
How to Design Applications to
Capitalize on the Data Explosion
Brady D. Lund
Daniel Agbaji
Kossi Dodzi Bissadu
Haihua Chen
ROWMAN & LITTLEFIELD
Lanham • Boulder • New York • London
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British Library Cataloguing in Publication Information Available
Library of Congress Cataloging-in-Publication Data
Names: Lund, Brady, 1994– author. | Agbaji, Daniel, author. | Bissadu, Kossi Dodzi,
author. | Chen, Haihua, author.
Title: Python for information professionals : how to design practical applications to
capitalize on the data explosion / Brady Lund, Daniel Agbaji, Kossi Dodzi Bissadu,
Haihua Chen.
Description: Lanham : Rowman & Littlefield Publishers, [2024] | Includes
bibliographical references and index.
Identifiers: LCCN 2023031250 (print) | LCCN 2023031251 (ebook) | ISBN
9781538178249 (cloth) | ISBN 9781538178256 (paperback) | ISBN 9781538178263
(ebook)
Subjects: LCSH: Python (Computer program language) | Libraries—Data processing.
Classification: LCC Z678.93.P98 L86 2024 (print) | LCC Z678.93.P98 (ebook) | DDC
025.00285—dc23/eng20231013
LC record available at https://lccn.loc.gov/2023031250
LC ebook record available at https://lccn.loc.gov/2023031251
The paper used in this publication meets the minimum requirements of American
National Standard for Information Sciences—Permanence of Paper for Printed Library
Materials, ANSI/NISO Z39.48-1992.
, Contents
Preface v
Part I: Python: The Basics
Chapter 1: The Python Workspace 3
Chapter 2: Object-Oriented Programming 13
Chapter 3: Data Types, Structures, Sets, and Algorithms 27
Chapter 4: Functions: Code That Puts Our Data to Work 37
Chapter 5: Importing, Creating, and Maintaining Data Files 47
Chapter 6: Testing and Troubleshooting 57
Part II: Further Applications of
Python in Information Organizations
Chapter 7: Library Management and Usage Data 69
Chapter 8: Library Research Data Management 81
Chapter 9: Text Analysis 91
Chapter 10: Library and Information Science Research 101
Chapter 11: Artificial Intelligence Applications 111
Part III: Practical and Ethical Considerations for Using Python
Chapter 12: Data Explosion, Big Data, and Data Literacy 121
Chapter 13: Data Ethics 129
iii