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Problem Solving with Algorithms and Data Structures using Python ¶
![problem solving with python pdf PythonDS Cover](https://runestone.academy/_images/PythonDScover.jpg)
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
![problem solving with python pdf Creative Commons License](https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png)
- 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
![problem solving with python pdf Creative Commons License](https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png)
Challenging Programming in Python: A Problem Solving Perspective
- © 2024
- Habib Izadkhah 0 ,
- Rashid Behzadidoost 1
Department of Computer Science, University of Tabriz, Tabriz, Iran
You can also search for this author in PubMed Google Scholar
- 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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About this book
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 focuson 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.
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Table of contents (8 chapters)
Front matter, introduction.
Habib Izadkhah, Rashid Behzadidoost
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Miscellaneous problems, authors and affiliations, about the authors.
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.
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Table of contents.
Title Contents Unit 1 Introduction to Computing and Algorithmic Problem Solving 1 Introduction to Digital Computer 2 Problem Solving Strategies Appendix A Practice Exercises with Algorithm and Flow Chart Appendix B Problem Solving Exercises with Algorithms and Pseudocode Unit 2 Introduction to Python and Data, Expressions, Statements 3 Introduction to Python Unit 3 Functions 4 Functions Unit 4 Lists, Tuples and Dictionaries 5 Strings and Lists 6 Tuples and Dictionaries Unit 5 Files, Modules and Packages 7 Files and Exceptions 8 Classes and Objects Appendix C Fundamental Standard Library Modules
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Algorithmic Problem Solving with Python John B. Schneider Shira Lynn Broschat Jess Dahmen February 22, 2019
Each exercise has 10-20 Questions. The solution is provided for every question. Practice each Exercise in Online Code Editor. These Python programming exercises are suitable for all Python developers. If you are a beginner, you will have a better understanding of Python after solving these exercises. Below is the list of exercises.
Problem Solving with Algorithms and Data Structures using Python¶. By Brad Miller and David Ranum, Luther College. Assignments; There is a wonderful collection of YouTube videos recorded by Gerry Jenkins to support all of the chapters in this text.
Python currently is one of the most popular programming languages, and as such, it has become vital for students to understand this concept in this language. This student-friendly textbook provides a complete view of data structures and algorithms using the Python programming language, striking a balance between theory and practical application.
An interactive version of Problem Solving with Algorithms and Data Structures using Python. ... Problem Solving with Algorithms and Data Structures using Python by Bradley N. Miller, David L. Ranum is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Next Section - 1. Introduction
About. Repo for the book Problem Solving with Python 3.7 Edition by Peter D. Kazarinoff, PhD. The newest edition of Problem Solving with Python book.
Course Description. CIS 1051 introduces students to computers, computer programming, and problem solving using programs written in the Python language. Topics covered include the general characteristics of computers; techniques of problem solving and algorithm specifications; and the implementation, debugging, and testing of computer programs.
Get started solving problems with the Python programming language!This book introduces some of the most famous scientific libraries for Python: * Python's math and statistics module to do calculations * Matplotlib to build 2D and 3D plots * NumPy to complete calculations on arrays * Jupiter Notebooks to share results with a team * SymPy to solve equations * PySerial to control an Arduino with ...
About this book. 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 ...
Problem Solving with Algorithms and Data Structures Using Python [Release 3.0].pdf. Latest commit ...
Python Practice Problem 5: Sudoku Solver. Your final Python practice problem is to solve a sudoku puzzle! Finding a fast and memory-efficient solution to this problem can be quite a challenge. The solution you'll examine has been selected for readability rather than speed, but you're free to optimize your solution as much as you want.
Apart from touching upon the concepts of Python programming, equal weightage in given on the implementation of these concepts in writing efficient python codes and solve problems using the same.Salient Features:- Comprehensive syllabus coverage of all major state and central universities- Clarity of concepts with suitable diagrams and ...
Unit 1 Introduction to Computing and Algorithmic Problem Solving 1 Introduction to Digital Computer 2 Problem Solving Strategies Appendix A Practice Exercises with Algorithm and Flow Chart Appendix B Problem Solving Exercises with Algorithms and Pseudocode Unit 2 Introduction to Python and Data, Expressions, Statements 3 Introduction to Python
Introduction To Computing And Problem Solving Using Python ... Computer Science, Python Collection opensource Language English Addeddate 2020-05-10 15:09:05 ... PDF download. download 1 file . SINGLE PAGE PROCESSED JP2 ZIP download. download 1 file ...
Problem solving technique is a set of techniques that helps in providing logic for solving a problem. Problem solving can be expressed in the form of 1. Algorithms. 2. Flowcharts. ... ELSE are the conditional structures used in Python language. •CASE is the structure used to select multi way selection control. It is not supported in Python ...
CSCI0030 Project 1: Problem Solving with Python Due : March 10th 2 Task 1: Proposal (due February 27th by 11:59pm) Write a concise description of the project you would like to execute before you start working with Python. Proposals should be 1-2 pages in length, but the more speci c details you provide, the better feedback we can give.
What is Problem solving? Problem solving is a process of transforming the description of a problem into the solution of that problem by using our knowledge of the problem domain and by relying on our ability to select and use appropriate problem-solving Strategies, Techniques and Tools. Problem solving (with in the context of
1.1Introduction to Problem Solving 1.1.1. Problem Analysis The key dimensions basis on which an appropriate method is applied are as follows Decomposable/non decomposable : Analyze whether the problem is decomposable, i.e, whether it can be broken into sub-problems. If cannot be decomposed then such problem is non-decomposable.
•To review the ideas of computer science, programming, and problem-solving. •To understand abstraction and the role it plays in the problem-solving process. •To understand and implement the notion of an abstract data type. •To review the Python programming language. 1.2Getting Started
An exceptionally broad range of topics, from simple matrix manipulations to intricate computations in nonlinear dynamics. A whole suite of supplementary material: Python programs, Jupyter notebooks and videos. Computational Physics is ideal for students in physics, engineering, materials science, and any subjects drawing on applied physics.
Easy Python (Basic) Max Score: 10 Success Rate: 89.71%. Solve Challenge. Arithmetic Operators. Easy Python (Basic) Max Score: 10 Success Rate: 97.41%. Solve Challenge. ... Problem Solving (Basic) Python (Basic) Problem Solving (Advanced) Python (Intermediate) Difficulty. Easy. Medium. Hard. Subdomains. Introduction. Basic Data Types. Strings ...