Data Structures and Algorithms with Python

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 algorithmssupported by motivating examplesthat bring meaning to the problems faced by computer programmers. The idea of computational complexity is introduced, demonstrating what can and cannot be computed efficiently at scale, helping programmers make informed judgements about the algorithms they use. The easy-to-read text assumes some basic experience in computer programming and familiarity in an object-oriented language, but not necessarily with Python.

Topics and features:

• Includes introductory and advanced data structures and algorithms topics, with suggested chapter sequences for those respective courses
• Provides learning goals, review questions, and programming exercises in each chapter, as well as numerous examples
• Presents a primer on Python for those coming from a different language background
• Adds a new chapter on multiprocessing with Python using the DragonHPC multinode implementation of multiprocessing (includes a tutorial)
• Reviews the use of hashing in sets and maps, and examines binary search trees, tree traversals, and select graph algorithms

• Offers downloadable programs and supplementary files at an associated website to help students Students of computer science will find this clear and concise textbook invaluable for undergraduate courses on data structures and algorithms, at both introductory and advanced levels. The book is also suitable as a refresher guide for computer programmers starting new jobs working with Python.

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

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

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

Auteur
Dr. Kent D. Lee is a Professor Emeritus of Computer Science at Luther College, Decorah, Iowa, USA. He now works for Hewlett Packard Enterprise as an Engineer and Architect on the DragonHPC project within the High Performance Computing division (formerly Cray, Inc.). He is the author of the successful introductory companion textbook from Springer, Python Programming Fundamentals, and the Foundations of Programming Languages - an excellent textbook on compiler and interpreter implementation.

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

Texte du rabat

This clearly structured and easy to read 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 motivating 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. The text assumes some basic experience in computer programming and familiarity in an object-oriented language, but not necessarily with Python.

Topics and 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 coming from a different language background; reviews the use of hashing in sets and maps, along with an examination of binary search trees and tree traversals, and material on depth first search of graphs; discusses topics suitable for an advanced course, such as membership structures, heaps, balanced binary search trees, B-trees and heuristic search.

Students of computer science will find this clear and concise textbook to be invaluable for undergraduate courses on data structures and algorithms, at both introductory and advanced levels. The book is also suitable as a refresher guide for computer programmers starting new jobs working with Python.

Contenu

1. Python Programming 101.- 2. Computational Complexity.- 3. Recursion.- 4. Sequences.- 5. Sets and Maps.- 6. Trees.- 7. Graphs.- 8. Membership Structures.- 9. Heaps.- 10. Balanced Binary Search Trees.- 11. B-Trees.- 12. Heuristic Search.