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  • Textbook
  • © 2015

Data Structures and Algorithms with Python

  • Includes broad coverage of both introductory and advanced data structures topics, supported by examples
  • Guides the reader through the concepts of computational complexity, from the basics to amortized complexity
  • Makes learning fun, using the development of graphical user interface programs to illustrate concepts
  • Includes supplementary material: sn.pub/extras

Part of the book series: Undergraduate Topics in Computer Science (UTICS)

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Table of contents (20 chapters)

  1. Front Matter

    Pages i-xv
  2. Python Programming 101

    • Kent D. Lee, Steve Hubbard
    Pages 1-40
  3. Computational Complexity

    • Kent D. Lee, Steve Hubbard
    Pages 41-65
  4. Recursion

    • Kent D. Lee, Steve Hubbard
    Pages 67-90
  5. Sequences

    • Kent D. Lee, Steve Hubbard
    Pages 91-138
  6. Sets and Maps

    • Kent D. Lee, Steve Hubbard
    Pages 139-161
  7. Trees

    • Kent D. Lee, Steve Hubbard
    Pages 163-183
  8. Graphs

    • Kent D. Lee, Steve Hubbard
    Pages 185-204
  9. Membership Structures

    • Kent D. Lee, Steve Hubbard
    Pages 205-214
  10. Heaps

    • Kent D. Lee, Steve Hubbard
    Pages 215-236
  11. Balanced Binary Search Trees

    • Kent D. Lee, Steve Hubbard
    Pages 237-260
  12. B-Trees

    • Kent D. Lee, Steve Hubbard
    Pages 261-280
  13. Heuristic Search

    • Kent D. Lee, Steve Hubbard
    Pages 281-297
  14. Appendix A: Integer Operators

    • Kent D. Lee, Steve Hubbard
    Pages 299-299
  15. Appendix B: Float Operators

    • Kent D. Lee, Steve Hubbard
    Pages 301-301
  16. Appendix C: String Operators and Methods

    • Kent D. Lee, Steve Hubbard
    Pages 303-306
  17. Appendix D: List Operators and Methods

    • Kent D. Lee, Steve Hubbard
    Pages 307-308
  18. Appendix E: Dictionary Operators and Methods

    • Kent D. Lee, Steve Hubbard
    Pages 309-310
  19. Appendix F: Turtle Methods

    • Kent D. Lee, Steve Hubbard
    Pages 311-321
  20. Appendix G: TurtleScreen Methods

    • Kent D. Lee, Steve Hubbard
    Pages 323-330

About this book

This textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents a number of important algorithms supported by examples that bring meaning to the problems faced by computer programmers. The idea of computational complexity is also introduced, demonstrating what can and cannot be computed efficiently so that the programmer can make informed judgements about the algorithms they use. Features: includes both introductory and advanced data structures and algorithms topics, with suggested chapter sequences for those respective courses provided in the preface; provides learning goals, review questions and programming exercises in each chapter, as well as numerous illustrative examples; offers downloadable programs and supplementary files at an associated website, with instructor materials available from the author; presents a primer on Python for those from a different language background.

Authors and Affiliations

  • Luther College, Decorah, USA

    Kent D. Lee, Steve Hubbard

About the authors

Dr. Kent D. Lee is Professor of Computer Science at Luther College, Decorah, Iowa, USA. He is the author of the successful Springer textbook Python Programming Fundamentals and the forthcoming Foundations of Programming Languages.

Dr. Steve Hubbard is Professor of Mathematics and Computer Science at Luther College.

Bibliographic Information

Buy it now

Buying options

eBook USD 44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Other ways to access