python problem solving pdf

Table of Contents
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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

Python Programming

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Basic Exercise for Beginners

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Topics : Variables, Operators, Loops, String, Numbers, List

Python Input and Output Exercise

Solve input and output operations in Python. Also, we practice file handling.

Topics : print() and input() , File I/O

Python Loop Exercise

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Topics : If-else statements, loop, and while loop.

Python Functions Exercise

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Topics : Functions arguments, built-in functions.

Python String Exercise

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Python Data Structure Exercise

Practice widely used Python types such as List, Set, Dictionary, and Tuple operations in Python

Python List Exercise

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Python Dictionary Exercise

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Python Set Exercise

This exercise aims to help Python developers to learn and practice set operations.

Python Tuple Exercise

This exercise aims to help Python developers to learn and practice tuple operations.

Python Date and Time Exercise

This exercise aims to help Python developers to learn and practice DateTime and timestamp questions and problems.

Topics : Date, time, DateTime, Calendar.

Python OOP Exercise

This Python Object-oriented programming (OOP) exercise aims to help Python developers to learn and practice OOP concepts.

Topics : Object, Classes, Inheritance

Python JSON Exercise

Practice and Learn JSON creation, manipulation, Encoding, Decoding, and parsing using Python

Python NumPy Exercise

Practice NumPy questions such as Array manipulations, numeric ranges, Slicing, indexing, Searching, Sorting, and splitting, and more.

Python Pandas Exercise

Practice Data Analysis using Python Pandas. Practice Data-frame, Data selection, group-by, Series, sorting, searching, and statistics.

Python Matplotlib Exercise

Practice Data visualization using Python Matplotlib. Line plot, Style properties, multi-line plot, scatter plot, bar chart, histogram, Pie chart, Subplot, stack plot.

Random Data Generation Exercise

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Python Database Exercise

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Use any of the MySQL, PostgreSQL, SQLite to solve the exercise

Exercises for Intermediate developers

The following practice questions are for intermediate Python developers.

If you have not solved the above exercises, please complete them to understand and practice each topic in detail. After that, you can solve the below questions quickly.

Exercise 1: Reverse each word of a string

Expected Output

Use the split() method to split a string into a list of words.
Reverse each word from a list
finally, use the join() function to convert a list into a string

Steps to solve this question :

Split the given string into a list of words using the split() method
Use a list comprehension to create a new list by reversing each word from a list.
Use the join() function to convert the new list into a string
Display the resultant string

Exercise 2: Read text file into a variable and replace all newlines with space

Given : Assume you have a following text file (sample.txt).

Expected Output :

First, read a text file.
Next, use string replace() function to replace all newlines ( \n ) with space ( ' ' ).

Steps to solve this question : -

First, open the file in a read mode
Next, read all content from a file using the read() function and assign it to a variable.
Display final string

Exercise 3: Remove items from a list while iterating

Description :

In this question, You need to remove items from a list while iterating but without creating a different copy of a list.

Remove numbers greater than 50

Expected Output : -

Get the list's size
Iterate list using while loop
Check if the number is greater than 50
If yes, delete the item using a del keyword
Reduce the list size

Solution 1: Using while loop

Solution 2: Using for loop and range()

Exercise 4: Reverse Dictionary mapping

Exercise 5: display all duplicate items from a list.

Use the counter() method of the collection module.
Create a dictionary that will maintain the count of each item of a list. Next, Fetch all keys whose value is greater than 2

Solution 1 : - Using collections.Counter()

Solution 2 : -

Exercise 6: Filter dictionary to contain keys present in the given list

Exercise 7: print the following number pattern.

Refer to Print patterns in Python to solve this question.

Use two for loops
The outer loop is reverse for loop from 5 to 0
Increment value of x by 1 in each iteration of an outer loop
The inner loop will iterate from 0 to the value of i of the outer loop
Print value of x in each iteration of an inner loop
Print newline at the end of each outer loop

Exercise 8: Create an inner function

Question description : -

Create an outer function that will accept two strings, x and y . ( x= 'Emma' and y = 'Kelly' .
Create an inner function inside an outer function that will concatenate x and y.
At last, an outer function will join the word 'developer' to it.

Exercise 9: Modify the element of a nested list inside the following list

Change the element 35 to 3500

Exercise 10: Access the nested key increment from the following dictionary

Under Exercises: -

Python Object-Oriented Programming (OOP) Exercise: Classes and Objects Exercises

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Title Problem Solving with Algorithms and Data Structures Using Python
Author(s) Brad Miller, David Ranum.
Publisher: Franklin, Beedle & Associates (2011), eBook (Creative Commons Edition, 2013)
License(s): CC BY-NC-SA 4.0
Hardcover/Papeback 438 pages
eBook HTML and PDF
Language: English
ISBN-10: 1590282574
ISBN-13: 978-1590282571

Tis textbook is about computer science. It is also about Python. However, there is much more. The study of algorithms and data structures is central to understanding what computer science is all about. Learning computer science is not unlike learning any other type of difficult subject matter.

The only way to be successful is through deliberate and incremental exposure to the fundamental ideas. A beginning computer scientist needs practice so that there is a thorough understanding before continuing on to the more complex parts of the curriculum. In addition, a beginner needs to be given the opportunity to be successful and gain confidence.

This textbook is designed to serve as a text for a first course on data structures and algorithms, typically taught as the second course in the computer science curriculum. Even though the second course is considered more advanced than the first course, this book assumes you are beginners at this level.

You may still be struggling with some of the basic ideas and skills from a first computer science course and yet be ready to further explore the discipline and continue to practice problem solving. We cover abstract data types and data structures, writing algorithms, and solving problems. We look at a number of data structures and solve classic problems that arise.

The tools and techniques that you learn here will be applied over and over as you continue your study of computer science.

Python Programming
Algorithms and Data Structures
Computational Complexity
Problem Solving with Algorithms and Data Structures Using Python (Brad Miller, et al)
The Mirror Site (1) - HTML
The Mirror Site (2) - PDF
The Mirror Site (3) - PDF
The Mirror Site (4) - PDF (770 pages)

This textbook serves as a gentle introduction for undergraduates to theoretical concepts in data structures and algorithms in computer science while providing coverage of practical implementation (coding) issues.

This introduction to computer programming with Python begins with some of the basics of computing and programming before diving into the fundamental elements and building blocks of computer programs in Python language.

This book covers Analysis and Design of Algorithms, Scientific Computing, Monte Carlo Simulations, and Parallel Algorithms. It teaches the core knowledge required by any scientist interested in numerical algorithms and computational finance.

This book uses Python to introduce folks to programming and algorithmic thinking. It is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques.

It promotes object-oriented design using Python and illustrates the use of the latest object-oriented design patterns. Virtually all the data structures are discussed in the context of a single class hierarchy.

Learn how to use Python to write programs that do in minutes what would take you hours to do by hand - no prior programming experience required. You'll create Python programs that effortlessly perform useful and impressive feats of automation.

This hands-on guide takes you through the Python programming language a step at a time, beginning with basic programming concepts before moving on to functions, recursion, data structures, and object-oriented design. 2nd edition updated for Python 3.

It focuses on introducing programming techniques and developing good habits. To that end, our approach avoids some of the more esoteric features of Python and concentrates on the programming basics that transfer directly to other imperative programming.

This book deepens your knowledge of problem-solving techniques from the realm of computer science by challenging you with time-tested scenarios, exercises, and algorithms. As you work through examples in search, clustering, graphs, and more.

The algorithmic approach to solving problems in computer technology is an essential tool. This book presents a readable, entertaining, and energetic book that will motivate and challenge students to open their minds to the algorithmic nature of problem solving.

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