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Programming and problem solving with python, 2nd edition - softcover, ashok kamthane, amit ashok kamthane.

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• Publisher MC GRAW HILL INDIA
• Publication date 2019
• ISBN 10  9390113024
• ISBN 13  9789390113026
• Binding Paperback
• Number of pages 536

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Problem Solving and Programming with Python

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Problem Solving with Algorithms and Data Structures using Python ¶

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 ¶

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Programming and Problem Solving with Python By.Kamthane (9789390113026)

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OVERVIEW The second edition of Programming and Problem Solving with Python is a hands-on-guide for engineering students from all streams, self-learners, and professionals to use Python language to perform complex calculations and write applications. The text is replete with programs and illustratively solved examples to explain all fundamental and theoretical concepts. The text develops problem-solving and code writing skills. This new edition is enhanced with new chapters, chapter-end and book-end projects and pedagogical elements while retaining the book’s original characteristics of lucid writing and illustrative explanation. All programs are thoroughly updated and checked for their compatibility with the latest versions of Python v3.8.

KEY FEATURES • Three new chapters on GUI and Python Programming using Tkinter, MySQL Database and Exception Handling • New projects on Book Inventory Management, Employee Database Management etc. • Pedagogical Features 1. 180+ Multiple Choice Questions 2. 220+ True or False Questions 3. 90+ Exercise Questions 4. 100 Programming Assignments

Chap 1: Introduction to Computer and Python Programming Chap 2: Basics of Python Programming Chap 3: Operators and Expressions Chap 4: Decision Statements Chap 5: Loop Control Statements Chap 6: Functions Chap 7: Strings Chap 8: Lists Chap 9: List Processing: Searching and Sorting Chap 10: Object-Oriented Programming: Class, Objects and Inheritance Chap 11: Tuples, Sets and Dictionaries Chap 12: Graphics Programming: Drawing with turtle Graphics Chap 13: File Handling Chap 14: Exception Handling Chap 15: GUI Python Programming using Tkinter Chap 16: Introduction to MySQL Database

Appendix I: Importing Modules in Python Appendix II: Project for Creating a Phone Book Directory Appendix III: Major Project on Book Inventory Management Appendix IV: Python Keywords Appendix V: ASCII Table Index

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Book description

A user-friendly, object-oriented language, Python is quickly becoming the favorite introductory programming language among students and instructors. Many find Python to be a more lucid language than Java but with much of the functionality and therefore the ideal first language for those entering the world of Computer Science. Python Programming in Context, Second Edition is a clear, accessible introduction to the fundamental programming and problem solving concepts necessary for students at this level. The authors carefully build upon the many important computer science concepts and problem solving techniques throughout the text and offer relevant, real-world examples and exercises to reinforce key material. Programming skills throughout the text are linked to applied areas such as Image Processing, Cryptography, Astronomy, Music, the Internet, and Bioinformatics, giving students a well-rounded look of its capabilities.

Table of contents

• Copyright Page
• Turing’s Craft CodeLab Student Registration Instructions
• 1.1 Objectives
• 1.2 What Is Computer Science?
• 1.3 Why Study Computer Science?
• 1.4 Problem-Solving Strategies
• 1.5 Python Overview
• 1.6 Summary
• 2.1 Objectives
• 2.2 What Is Pi?
• 2.3 More About the math Module
• 2.4 The Archimedes Approach
• 2.5 Accumulator Approximations
• 2.6 A Monte Carlo Simulation
• 2.7 Summary
• 3.1 Objectives
• 3.2 Introduction
• 3.3 Encoding and Decoding Messages
• 3.4 Transposition Cipher
• 3.5 Substitution Cipher
• 3.6 Creating a Key
• 3.7 The Vignère Cipher
• 3.8 Summary
• 4.1 Objectives
• 4.2 What Is Data?
• 4.3 Storing Data for Processing
• 4.4 Simple Dispersion
• 4.5 Central Tendency
• 4.6 Frequency Distribution
• 4.7 Dispersion: Standard Deviation
• 4.8 Summary
• 5.1 Objectives
• 5.2 Using Files for Large Data Sets
• 5.3 Reading Data from the Internet
• 5.4 Summary
• 6.1 Objectives
• 6.2 What Is Digital Image Processing?
• 6.3 Basic Image Processing
• 6.4 Parameters, Parameter Passing, and Scope
• 6.5 Advanced Image Processing
• 6.6 Summary
• 7.1 Objectives
• 7.2 What Is Data Mining?
• 7.3 Cluster Analysis: A Simple Example
• 7.4 Implementing Cluster Analysis on Simple Data
• 7.5 Implementing Cluster Analysis: Earthquakes
• 7.6 Cluster Analysis Shortcomings and Solutions
• 7.7 Summary
• 8.1 Objectives
• 8.2 Introduction
• 8.3 Cracking the Rail Fence
• 8.4 Cracking the Substitution Cipher
• 8.5 Summary
• 9.1 Objectives
• 9.2 Introduction
• 9.3 Recursive Programs
• 9.4 Snowflakes, Lindenmayer, and Grammars
• 9.5 Summary
• 10.1 Objectives
• 10.2 Introduction
• 10.3 Designing and Implementing a Planet Class
• 10.4 Designing and Implementing a Sun Class
• 10.5 Designing and Implementing a Solar System
• 10.6 Animating the Solar System
• 10.7 Summary
• 11.1 Objectives
• 11.2 Bears and Fish
• 11.3 What Is a Simulation?
• 11.4 Rules of the Game
• 11.5 Design
• 11.6 Implementation
• 11.7 Growing Plants
• 11.8 A Note on Inheritance
• 11.9 Summary
• 12.1 Objectives
• 12.2 Introduction
• 12.3 First Design
• 12.4 Basic Implementation
• 12.5 Understanding Inheritance
• 12.6 Limitations
• 12.7 An Improved Implementation
• 12.8 Implementing Polygons
• 12.9 Summary
• 13.1 Objectives
• 13.2 Introduction
• 13.3 Event-Driven Programming with turtle
• 13.4 Creating Your Own Video Game
• 13.5 Summary
• Appendix A: Installing the Required Software
• Appendix B: Python Quick Reference
• Appendix C: turtle Reference

Product information

• Title: Python Programming in Context, 2nd Edition
• Author(s): Bradley N. Miller, David L. Ranum
• Release date: January 2013
• Publisher(s): Jones & Bartlett Learning
• ISBN: 9781449691981

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