Data structures and algorithms in python book

Data Structures and Algorithms with Python

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Table of contents (20 chapters)

Front Matter

Python Programming 101

Computational Complexity

Recursion

Sequences

Sets and Maps

Trees

Graphs

Membership Structures

Heaps

Balanced Binary Search Trees

B-Trees

Heuristic Search

Appendix A: Integer Operators

Appendix B: Float Operators

Appendix C: String Operators and Methods

Appendix D: List Operators and Methods

Appendix E: Dictionary Operators and Methods

Appendix F: Turtle Methods

Appendix G: TurtleScreen Methods

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.

Keywords

• Algorithms
• Computational Complexity
• Data Structures
• Programming
• Python
• algorithm analysis and problem complexity

Authors and Affiliations

Luther College, Decorah, USA

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

• Book Title : Data Structures and Algorithms with Python
• Authors : Kent D. Lee, Steve Hubbard
• Series Title : Undergraduate Topics in Computer Science
• DOI : https://doi.org/10.1007/978-3-319-13072-9
• Publisher : Springer Cham
• eBook Packages : Computer Science , Computer Science (R0)
• Copyright Information : Springer Nature Switzerland AG 2015
• Softcover ISBN : 978-3-319-13071-2 Published: 22 January 2015
• eBook ISBN : 978-3-319-13072-9 Published: 12 January 2015
• Series ISSN : 1863-7310
• Series E-ISSN : 2197-1781
• Edition Number : 1
• Number of Pages : XV, 363
• Number of Illustrations : 8 b/w illustrations, 139 illustrations in colour
• Topics : Data Science , Python , Algorithms , Programming Techniques

Источник

Data Structures and Algorithms in Python

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Book description

Based on the authors’ market leading data structures books in Java and C++, this textbook offers a comprehensive, definitive introduction to data structures in Python by respected authors. Data Structures and Algorithms in Python is the first mainstream object-oriented book available for the Python data structures course. Designed to provide a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation, the text will maintain the same general structure as Data Structures and Algorithms in Java and Data Structures and Algorithms in C++.

Table of contents

1. Cover Page
2. Title Page
3. Copyright
4. Dedication
5. Preface
   1. Book Features
   2. Online Resources
   3. Contents and Organization
   4. Prerequisites
   5. Relation to Computer Science Curriculum
   6. About the Authors
   7. Additional Books by These Authors
   8. Acknowledgments
   1. 1.1 Python Overview
   2. 1.2 Objects in Python
   3. 1.3 Expressions, Operators, and Precedence
   4. 1.4 Control Flow
   5. 1.5 Functions
   6. 1.5.1 Information Passing
   7. 1.6 Simple Input and Output
   8. 1.7 Exception Handling
   9. 1.8 Iterators and Generators
   10. 1.9 Additional Python Conveniences
   11. 1.10 Scopes and Namespaces
   12. 1.11 Modules and the Import Statement
   13. 1.12 Exercises
   1. 2.1 Goals, Principles, and Patterns
   2. 2.2 Software Development
   3. 2.3 Class Definitions
   4. 2.4 Inheritance
   5. 2.5 Namespaces and Object-Orientation
   6. 2.6 Shallow and Deep Copying
   7. 2.7 Exercises
   1. 3.1 Experimental Studies
   2. 3.2 The Seven Functions Used in This Book
   3. 3.3 Asymptotic Analysis
   4. 3.4 Simple Justification Techniques
   5. 3.5 Exercises
   1. 4.1 Illustrative Examples
   2. 4.2 Analyzing Recursive Algorithms
   3. 4.3 Recursion Run Amok
   4. 4.4 Further Examples of Recursion
   5. 4.5 Designing Recursive Algorithms
   6. 4.6 Eliminating Tail Recursion
   7. 4.7 Exercises
   1. 5.1 Python’s Sequence Types
   2. 5.2 Low-Level Arrays
   3. 5.3 Dynamic Arrays and Amortization
   4. 5.4 Efficiency of Python’s Sequence Types
   5. 5.5 Using Array-Based Sequences
   6. 5.6 Multidimensional Data Sets
   7. 5.7 Exercises
   1. 6.1 Stacks
   2. 6.2 Queues
   3. 6.3 Double-Ended Queues
   4. 6.4 Exercises
   1. 7.1 Singly Linked Lists
   2. 7.2 Circularly Linked Lists
   3. 7.3 Doubly Linked Lists
   4. 7.4 The Positional List ADT
   5. 7.5 Sorting a Positional List
   6. 7.6 Case Study: Maintaining Access Frequencies
   7. 7.7 Link-Based vs. Array-Based Sequences
   8. 7.8 Exercises
   1. 8.1 General Trees
   2. 8.2 Binary Trees
   3. 8.3 Implementing Trees
   4. 8.4 Tree Traversal Algorithms
   5. 8.5 Case Study: An Expression Tree
   6. 8.6 Exercises
   1. 9.1 The Priority Queue Abstract Data Type
   2. 9.2 Implementing a Priority Queue
   3. 9.3 Heaps
   4. 9.4 Sorting with a Priority Queue
   5. 9.5 Adaptable Priority Queues
   6. 9.6 Exercises
   1. 10.1 Maps and Dictionaries
   2. 10.2 Hash Tables
   3. 10.3 Sorted Maps
   4. 10.4 Skip Lists
   5. 10.5 Sets, Multisets, and Multimaps
   6. 10.6 Exercises
   1. 11.1 Binary Search Trees
   2. 11.2 Balanced Search Trees
   3. 11.3 AVL Trees
   4. 11.4 Splay Trees
   5. 11.5 (2,4) Trees
   6. 11.6 Red-Black Trees
   7. 11.7 Exercises
   1. 12.1 Why Study Sorting Algorithms?
   2. 12.2 Merge-Sort
   3. 12.3 Quick-Sort
   4. 12.4 Studying Sorting through an Algorithmic Lens
   5. 12.5 Comparing Sorting Algorithms
   6. 12.6 Python’s Built-In Sorting Functions
   7. 12.7 Selection
   8. 12.8 Exercises
   1. 13.1 Abundance of Digitized Text
   2. 13.2 Pattern-Matching Algorithms
   3. 13.3 Dynamic Programming
   4. 13.4 Text Compression and the Greedy Method
   5. 13.5 Tries
   6. 13.6 Exercises
   1. 14.1 Graphs
   2. 14.2 Data Structures for Graphs
   3. 14.3 Graph Traversals
   4. 14.4 Transitive Closure
   5. 14.5 Directed Acyclic Graphs
   6. 14.6 Shortest Paths
   7. 14.7 Minimum Spanning Trees
   8. 14.8 Exercises
   1. 15.1 Memory Management
   2. 15.2 Memory Hierarchies and Caching
   3. 15.3 External Searching and B-Trees
   4. 15.4 External-Memory Sorting
   5. 15.5 Exercises

   Product information

   • Title: Data Structures and Algorithms in Python
   • Author(s):
   • Release date: March 2013
   • Publisher(s): Wiley
   • ISBN: 9781118290279

   Источник

   Best Python Data Structures And Algorithms Books

   Last updated June 6, 2023 by Jarvis Silva In this article I will share with you the best python data structures and algorithms books to read if you want to learn DSA, Learning data structures and algorithms can be very difficult for beginners, some DSA concepts are complex and difficult to understand. DSA is very important if you want to get a job or improve your programming skill so you need to learn them as a programmer if you are python programmer then you can learn DSA in python

   Best books for data structures and algorithms in python

   • Data Structures and Algorithms in Python Book.
   • Data Structure and Algorithmic Thinking with Python.
   • Problem Solving with Algorithms and Data Structures Using Python.
   • Python Data Structures and Algorithms Book.

   1. Data Structures and Algorithms in Python Book

   

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   2. Data Structure and Algorithmic Thinking with Python

   

   This book is written by Narasimha Karumanchi, who is a software developer and has written many books on DSA. This book covers all the data structures and algorithms topics from beginning to advanced.

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   Author: Bradley N Miller

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   4. Python Data Structures and Algorithms Book

   

   This book is written by three authors who are very specialized in DSA. This book provides a definitive introduction, design and analysis of data structures and algorithms in python programming.

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   Ratings: 4.5 out of 5

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   Is Python Good or Bad For DSA?

   As I have, you can learn data structures and algorithms in any programming language, it does not matter. If you understand the DSA concepts, you can use it in any programming language to solve problems.

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   Which Is The Best Book Data Structures And Algorithms Book In Python?

   From all the DSA books I have mentioned, in my opinion the best DSA book in python is Data Structure and Algorithmic Thinking with Python Book.

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   Summary

   These were all the best python dsa books you can read, All these books are available on Amazon to purchase, you can visit the link and get them.

   Here are some more book guides in python :

   I hope you found this article helpful and you found the books you were looking for. We will update more books in this article in the future, to get updates join our Telegram channel.

   Thank you for reading, have a nice day 🙂

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