python problem solving book pdf

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Problem Solving with Algorithms and Data Structures using Python ¶

PythonDS Cover

By Brad Miller and David Ranum, Luther College

There is a wonderful collection of YouTube videos recorded by Gerry Jenkins to support all of the chapters in this text.

  • 1.1. Objectives
  • 1.2. Getting Started
  • 1.3. What Is Computer Science?
  • 1.4. What Is Programming?
  • 1.5. Why Study Data Structures and Abstract Data Types?
  • 1.6. Why Study Algorithms?
  • 1.7. Review of Basic Python
  • 1.8.1. Built-in Atomic Data Types
  • 1.8.2. Built-in Collection Data Types
  • 1.9.1. String Formatting
  • 1.10. Control Structures
  • 1.11. Exception Handling
  • 1.12. Defining Functions
  • 1.13.1. A Fraction Class
  • 1.13.2. Inheritance: Logic Gates and Circuits
  • 1.14. Summary
  • 1.15. Key Terms
  • 1.16. Discussion Questions
  • 1.17. Programming Exercises
  • 2.1.1. A Basic implementation of the MSDie class
  • 2.2. Making your Class Comparable
  • 3.1. Objectives
  • 3.2. What Is Algorithm Analysis?
  • 3.3. Big-O Notation
  • 3.4.1. Solution 1: Checking Off
  • 3.4.2. Solution 2: Sort and Compare
  • 3.4.3. Solution 3: Brute Force
  • 3.4.4. Solution 4: Count and Compare
  • 3.5. Performance of Python Data Structures
  • 3.7. Dictionaries
  • 3.8. Summary
  • 3.9. Key Terms
  • 3.10. Discussion Questions
  • 3.11. Programming Exercises
  • 4.1. Objectives
  • 4.2. What Are Linear Structures?
  • 4.3. What is a Stack?
  • 4.4. The Stack Abstract Data Type
  • 4.5. Implementing a Stack in Python
  • 4.6. Simple Balanced Parentheses
  • 4.7. Balanced Symbols (A General Case)
  • 4.8. Converting Decimal Numbers to Binary Numbers
  • 4.9.1. Conversion of Infix Expressions to Prefix and Postfix
  • 4.9.2. General Infix-to-Postfix Conversion
  • 4.9.3. Postfix Evaluation
  • 4.10. What Is a Queue?
  • 4.11. The Queue Abstract Data Type
  • 4.12. Implementing a Queue in Python
  • 4.13. Simulation: Hot Potato
  • 4.14.1. Main Simulation Steps
  • 4.14.2. Python Implementation
  • 4.14.3. Discussion
  • 4.15. What Is a Deque?
  • 4.16. The Deque Abstract Data Type
  • 4.17. Implementing a Deque in Python
  • 4.18. Palindrome-Checker
  • 4.19. Lists
  • 4.20. The Unordered List Abstract Data Type
  • 4.21.1. The Node Class
  • 4.21.2. The Unordered List Class
  • 4.22. The Ordered List Abstract Data Type
  • 4.23.1. Analysis of Linked Lists
  • 4.24. Summary
  • 4.25. Key Terms
  • 4.26. Discussion Questions
  • 4.27. Programming Exercises
  • 5.1. Objectives
  • 5.2. What Is Recursion?
  • 5.3. Calculating the Sum of a List of Numbers
  • 5.4. The Three Laws of Recursion
  • 5.5. Converting an Integer to a String in Any Base
  • 5.6. Stack Frames: Implementing Recursion
  • 5.7. Introduction: Visualizing Recursion
  • 5.8. Sierpinski Triangle
  • 5.9. Complex Recursive Problems
  • 5.10. Tower of Hanoi
  • 5.11. Exploring a Maze
  • 5.12. Dynamic Programming
  • 5.13. Summary
  • 5.14. Key Terms
  • 5.15. Discussion Questions
  • 5.16. Glossary
  • 5.17. Programming Exercises
  • 6.1. Objectives
  • 6.2. Searching
  • 6.3.1. Analysis of Sequential Search
  • 6.4.1. Analysis of Binary Search
  • 6.5.1. Hash Functions
  • 6.5.2. Collision Resolution
  • 6.5.3. Implementing the Map Abstract Data Type
  • 6.5.4. Analysis of Hashing
  • 6.6. Sorting
  • 6.7. The Bubble Sort
  • 6.8. The Selection Sort
  • 6.9. The Insertion Sort
  • 6.10. The Shell Sort
  • 6.11. The Merge Sort
  • 6.12. The Quick Sort
  • 6.13. Summary
  • 6.14. Key Terms
  • 6.15. Discussion Questions
  • 6.16. Programming Exercises
  • 7.1. Objectives
  • 7.2. Examples of Trees
  • 7.3. Vocabulary and Definitions
  • 7.4. List of Lists Representation
  • 7.5. Nodes and References
  • 7.6. Parse Tree
  • 7.7. Tree Traversals
  • 7.8. Priority Queues with Binary Heaps
  • 7.9. Binary Heap Operations
  • 7.10.1. The Structure Property
  • 7.10.2. The Heap Order Property
  • 7.10.3. Heap Operations
  • 7.11. Binary Search Trees
  • 7.12. Search Tree Operations
  • 7.13. Search Tree Implementation
  • 7.14. Search Tree Analysis
  • 7.15. Balanced Binary Search Trees
  • 7.16. AVL Tree Performance
  • 7.17. AVL Tree Implementation
  • 7.18. Summary of Map ADT Implementations
  • 7.19. Summary
  • 7.20. Key Terms
  • 7.21. Discussion Questions
  • 7.22. Programming Exercises
  • 8.1. Objectives
  • 8.2. Vocabulary and Definitions
  • 8.3. The Graph Abstract Data Type
  • 8.4. An Adjacency Matrix
  • 8.5. An Adjacency List
  • 8.6. Implementation
  • 8.7. The Word Ladder Problem
  • 8.8. Building the Word Ladder Graph
  • 8.9. Implementing Breadth First Search
  • 8.10. Breadth First Search Analysis
  • 8.11. The Knight’s Tour Problem
  • 8.12. Building the Knight’s Tour Graph
  • 8.13. Implementing Knight’s Tour
  • 8.14. Knight’s Tour Analysis
  • 8.15. General Depth First Search
  • 8.16. Depth First Search Analysis
  • 8.17. Topological Sorting
  • 8.18. Strongly Connected Components
  • 8.19. Shortest Path Problems
  • 8.20. Dijkstra’s Algorithm
  • 8.21. Analysis of Dijkstra’s Algorithm
  • 8.22. Prim’s Spanning Tree Algorithm
  • 8.23. Summary
  • 8.24. Key Terms
  • 8.25. Discussion Questions
  • 8.26. Programming Exercises

Acknowledgements ¶

We are very grateful to Franklin Beedle Publishers for allowing us to make this interactive textbook freely available. This online version is dedicated to the memory of our first editor, Jim Leisy, who wanted us to “change the world.”

Indices and tables ¶

Search Page

Creative Commons License

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Real World Python: A Hacker's Guide To Solving Problems With Code (PDF Ebook)

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Challenging Programming in Python: A Problem Solving Perspective

  • Habib Izadkhah 0 ,
  • Rashid Behzadidoost 1

Department of Computer Science, University of Tabriz, Tabriz, Iran

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Demonstrates with a lot of examples how to solve problems with Python

Provides an accessible introduction to Python

Presents challenging problems and its programming solutions from different areas of applied sciences

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Table of contents (8 chapters)

Front matter, introduction.

Habib Izadkhah, Rashid Behzadidoost

Python Basics

Miscellaneous problems.

This book aims to strengthen programming skills and foster creative thinking by presenting and solving 90 challenging problems. The book is intended for individuals with elementary, intermediate, and advanced Python programming skills who aspire to take their abilities to the next level. Additionally, the book is valuable for individuals interested in enhancing their creative thinking and logical reasoning skills. It is a self-instructional book meant to provide readers with the ability to solve challenging problems independently. The presented challenges are lucidly and succinctly expressed, facilitating readers to follow along and comprehend the problem-solving process. The challenges cover various fields, making it suitable for a wide range of individuals.

The book is divided into eight chapters, beginning with an introduction in chapter one. The second chapter presents essential Python basics for programming challenging problems, while the subsequent chapters focus on specific types of challenges. These include math-based challenges in chapter three, number-based challenges in chapter four, string-based challenges in chapter five, game-based challenges in chapter six, count-based challenges in chapter seven, and miscellaneous challenges in chapter eight. Each chapter comprises a set of challenges with examples, hints, algorithms, and Python code solutions. The target audience of the book includes computer science and engineering students, teachers, software developers, and participants in programming competitions.

  • Problem Solving with Phyton
  • Programming Language
  • Introduction to Phyton
  • Phyton Basics

Dr. Habib Izadkhah is an associate professor at the Department of Computer Science, University of Tabriz, Iran. He worked in the industry for a decade as a software engineer before becoming an academic. His research interests include algorithms and graphs, software engineering, and bioinformatics. More recently, he has been working on developing and applying deep learning to a variety of problems, dealing with biomedical images, speech recognition, text understanding, and generative models. He has contributed to various research projects, authored a number of research papers in international conferences, workshops, and journals, and also has written five books, including Source Code Modularization: Theory and Techniques from Springer and Deep Learning in Bioinformatics from Elsevier.

Rashid Behzadidoost is a Ph.D. candidate in Computer Science at the University of Tabriz, Iran. He is currently pursuing his doctoral degree in Computer Science, specializing in artificial intelligence and natural language processing. Rashid has a deep passion for coding and enjoys solving challenging problems. He has obtained his skills through years of study, practice, and teaching. He has taught several courses on computer sciences including challenging programming, microprocessor, and data structure at the University of Tabriz.

Book Title : Challenging Programming in Python: A Problem Solving Perspective

Authors : Habib Izadkhah, Rashid Behzadidoost

DOI : https://doi.org/10.1007/978-3-031-39999-2

Publisher : Springer Cham

eBook Packages : Engineering , Engineering (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024

Hardcover ISBN : 978-3-031-39998-5 Published: 18 October 2023

Softcover ISBN : 978-3-031-40001-8 Due: 01 November 2024

eBook ISBN : 978-3-031-39999-2 Published: 17 October 2023

Edition Number : 1

Number of Pages : XI, 280

Number of Illustrations : 3 b/w illustrations, 7 illustrations in colour

Topics : Mathematical and Computational Engineering , Control, Robotics, Mechatronics , Professional Computing

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Problem Solving with Python

If you like this book, please consider purchasing a hard copy version on amazon.com .

  • You will find the book chapters on the left hand menu
  • You will find navigation within a section of a chapter (one webpage) on the righthand menu
  • Sources for this text are stored on GitHub at github.com/professorkazarinoff/Problem-Solving-with-Python-37-Edition

If you find the text useful, please consider supporting the work by purchasing a hard copy of the text .

This work is licensed under a GNU General Public License v3.0

  • Table of Contents
  • Scratch ActiveCode
  • Navigation Help
  • Help for Instructors
  • About Runestone
  • Report A Problem
  • 1. Introduction
  • 2. Analysis
  • 3. Basic Data Structures
  • 4. Recursion
  • 5. Sorting and Searching
  • 6. Trees and Tree Algorithms
  • 7. Graphs and Graph Algorithms

Problem Solving with Algorithms and Data Structures using Python ¶

By Brad Miller and David Ranum, Luther College (as remixed by Jeffrey Elkner)

  • 1.1. Objectives
  • 1.2. Getting Started
  • 1.3. What Is Computer Science?
  • 1.4. What Is Programming?
  • 1.5. Why Study Data Structures and Abstract Data Types?
  • 1.6. Why Study Algorithms?
  • 1.7. Review of Basic Python
  • 1.8.1. Built-in Atomic Data Types
  • 1.8.2. Built-in Collection Data Types
  • 1.9.1. String Formatting
  • 1.10. Control Structures
  • 1.11. Exception Handling
  • 1.12. Defining Functions
  • 1.13.1. A Fraction Class
  • 1.13.2. Inheritance: Logic Gates and Circuits
  • 1.14. Summary
  • 1.15. Key Terms
  • 1.16. Discussion Questions
  • 1.17. Programming Exercises
  • 2.1. Objectives
  • 2.2. What Is Algorithm Analysis?
  • 2.3. Big-O Notation
  • 2.4.1. Solution 1: Checking Off
  • 2.4.2. Solution 2: Sort and Compare
  • 2.4.3. Solution 3: Brute Force
  • 2.4.4. Solution 4: Count and Compare
  • 2.5. Performance of Python Data Structures
  • 2.7. Dictionaries
  • 2.8. Summary
  • 2.9. Key Terms
  • 2.10. Discussion Questions
  • 2.11. Programming Exercises
  • 3.1. Objectives
  • 3.2. What Are Linear Structures?
  • 3.3. What is a Stack?
  • 3.4. The Stack Abstract Data Type
  • 3.5. Implementing a Stack in Python
  • 3.6. Simple Balanced Parentheses
  • 3.7. Balanced Symbols (A General Case)
  • 3.8. Converting Decimal Numbers to Binary Numbers
  • 3.9.1. Conversion of Infix Expressions to Prefix and Postfix
  • 3.9.2. General Infix-to-Postfix Conversion
  • 3.9.3. Postfix Evaluation
  • 3.10. What Is a Queue?
  • 3.11. The Queue Abstract Data Type
  • 3.12. Implementing a Queue in Python
  • 3.13. Simulation: Hot Potato
  • 3.14.1. Main Simulation Steps
  • 3.14.2. Python Implementation
  • 3.14.3. Discussion
  • 3.15. What Is a Deque?
  • 3.16. The Deque Abstract Data Type
  • 3.17. Implementing a Deque in Python
  • 3.18. Palindrome-Checker
  • 3.19. Lists
  • 3.20. The Unordered List Abstract Data Type
  • 3.21.1. The Node Class
  • 3.21.2. The Unordered List Class
  • 3.22. The Ordered List Abstract Data Type
  • 3.23.1. Analysis of Linked Lists
  • 3.24. Summary
  • 3.25. Key Terms
  • 3.26. Discussion Questions
  • 3.27. Programming Exercises
  • 4.1. Objectives
  • 4.2. What Is Recursion?
  • 4.3. Calculating the Sum of a List of Numbers
  • 4.4. The Three Laws of Recursion
  • 4.5. Converting an Integer to a String in Any Base
  • 4.6. Stack Frames: Implementing Recursion
  • 4.7. Introduction: Visualizing Recursion
  • 4.8. Sierpinski Triangle
  • 4.9. Complex Recursive Problems
  • 4.10. Tower of Hanoi
  • 4.11. Exploring a Maze
  • 4.12. Dynamic Programming
  • 4.13. Summary
  • 4.14. Key Terms
  • 4.15. Discussion Questions
  • 4.16. Glossary
  • 4.17. Programming Exercises
  • 5.1. Objectives
  • 5.2. Searching
  • 5.3.1. Analysis of Sequential Search
  • 5.4.1. Analysis of Binary Search
  • 5.5.1. Hash Functions
  • 5.5.2. Collision Resolution
  • 5.5.3. Implementing the Map Abstract Data Type
  • 5.5.4. Analysis of Hashing
  • 5.6. Sorting
  • 5.7. The Bubble Sort
  • 5.8. The Selection Sort
  • 5.9. The Insertion Sort
  • 5.10. The Shell Sort
  • 5.11. The Merge Sort
  • 5.12. The Quick Sort
  • 5.13. Summary
  • 5.14. Key Terms
  • 5.15. Discussion Questions
  • 5.16. Programming Exercises
  • 6.1. Objectives
  • 6.2. Examples of Trees
  • 6.3. Vocabulary and Definitions
  • 6.4. List of Lists Representation
  • 6.5. Nodes and References
  • 6.6. Parse Tree
  • 6.7. Tree Traversals
  • 6.8. Priority Queues with Binary Heaps
  • 6.9. Binary Heap Operations
  • 6.10.1. The Structure Property
  • 6.10.2. The Heap Order Property
  • 6.10.3. Heap Operations
  • 6.11. Binary Search Trees
  • 6.12. Search Tree Operations
  • 6.13. Search Tree Implementation
  • 6.14. Search Tree Analysis
  • 6.15. Balanced Binary Search Trees
  • 6.16. AVL Tree Performance
  • 6.17. AVL Tree Implementation
  • 6.18. Summary of Map ADT Implementations
  • 6.19. Summary
  • 6.20. Key Terms
  • 6.21. Discussion Questions
  • 6.22. Programming Exercises
  • 7.1. Objectives
  • 7.2. Vocabulary and Definitions
  • 7.3. The Graph Abstract Data Type
  • 7.4. An Adjacency Matrix
  • 7.5. An Adjacency List
  • 7.6. Implementation
  • 7.7. The Word Ladder Problem
  • 7.8. Building the Word Ladder Graph
  • 7.9. Implementing Breadth First Search
  • 7.10. Breadth First Search Analysis
  • 7.11. The Knight’s Tour Problem
  • 7.12. Building the Knight’s Tour Graph
  • 7.13. Implementing Knight’s Tour
  • 7.14. Knight’s Tour Analysis
  • 7.15. General Depth First Search
  • 7.16. Depth First Search Analysis
  • 7.17. Topological Sorting
  • 7.18. Strongly Connected Components
  • 7.19. Shortest Path Problems
  • 7.20. Dijkstra’s Algorithm
  • 7.21. Analysis of Dijkstra’s Algorithm
  • 7.22. Prim’s Spanning Tree Algorithm
  • 7.23. Summary
  • 7.24. Key Terms
  • 7.25. Discussion Questions
  • 7.26. Programming Exercises

Acknowledgements ¶

We are very grateful to Franklin Beedle Publishers for allowing us to make this interactive textbook freely available. This online version is dedicated to the memory of our first editor, Jim Leisy, who wanted us to “change the world.”

Indices and tables ¶

  • Module Index
  • Search Page

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Introduction To Computing And Problem Solving Using Python PDF

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Table of contents.

Title CONTENTS 1 INTRODUCTION TO DIGITAL COMPUTER 2 PROBLEM SOLVING STRATEGIES 3 INTRODUCTION TO PYTHON 4 FUNCTIONS 5 STRINGS AND LISTS 6 TUPLES AND DICTIONARIES 7 FILES AND EXCEPTIONS 8 CLASSES AND OBJECTS Appedix A Appendix B Appendix C

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  15. ProfessorKazarinoff/Problem-Solving-with-Python-37-Edition

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  19. Introduction to Computer Science Using Python: A Computational Problem

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  20. Introduction To Computing And Problem Solving Using Python PDF

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  21. (PDF) Fundamentals of Problem Solving and Python Programming

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