algorithmic problem solving python

• Runestone in social media: Follow @iRunestone Our Facebook Page
• Table of Contents
• Assignments
• Peer Instruction (Instructor)
• Peer Instruction (Student)
• Change Course
• Instructor's Page
• Progress Page
• Edit Profile
• Change Password
• Scratch ActiveCode
• Scratch Activecode
• Instructors Guide
• About Runestone
• Report A Problem
• This Chapter
• 1. Introduction' data-toggle="tooltip" >

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 ¶

Search Page

School of Electrical Engineering and Computer Science Washington State University

Algorithmic problem solving with python.

• 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.”

How to Solve Algorithmic Problems in Python

Understanding the Basics of Algorithmic Problems

An algorithm is a step-by-step procedure for solving a problem or accomplishing a specific task. Algorithmic problems are challenges that require an algorithm to solve. These challenges often involve data manipulation or optimization.

Step 1: Understand the Problem

The first step in solving any problem, algorithmic or otherwise, is to understand the problem thoroughly. This involves identifying the problem's inputs, expected outputs, and constraints.

Step 2: Design the Algorithm

Once you understand the problem, the next step is to design an algorithm to solve it. This involves breaking down the problem into smaller, more manageable parts and designing a solution for each.

Example: Solving a Sorting Problem

Let's look at a simple algorithmic problem: sorting a list of numbers in ascending order. Here is a Python function that uses the Bubble Sort algorithm to solve this problem:

The bubble_sort function sorts the list of numbers by repeatedly swapping adjacent numbers if they are in the wrong order.

Step 3: Implement the Algorithm

After designing the algorithm, you must implement it in Python. This involves writing Python code that follows the steps outlined in your algorithm.

The code above creates a list of numbers, sorts it using the bubble_sort function, and then prints the sorted list.

Step 4: Test the Algorithm

After implementing the algorithm, you should test it to ensure it works correctly. This involves running your Python code and checking that it produces the expected output for various input values.

The code snippet above shows the output of running the bubble_sort.py script in a shell. As you can see, it correctly sorts the list of numbers.

Step 5: Analyze the Algorithm

You should analyze your algorithm to understand its efficiency. This involves calculating your algorithm's time and space complexity, which are measures of how the algorithm's resource usage scales with the input size.

For Bubble Sort, the time complexity is O(n^2), and the space complexity is O(1). This means Bubble Sort can be inefficient for large lists but uses a small, constant amount of memory.

In conclusion, solving algorithmic problems in Python involves understanding the problem, designing an algorithm to solve it, implementing it in Python, testing it, and analyzing its efficiency. This process is a fundamental part of Python programming and general software development. If you're looking to hire Python developers who are proficient in solving algorithmic problems, check out Reintech.

If you're interested in enhancing this article or becoming a contributing author, we'd love to hear from you.

Please contact Sasha at [email protected] to discuss the opportunity further or to inquire about adding a direct link to your resource. We welcome your collaboration and contributions!

An algorithm is a step-by-step procedure or set of instructions designed to solve a problem or achieve a specific goal. In computer programming, algorithms are used to perform tasks, manipulate data, and make decisions based on certain conditions. Developing efficient algorithms is a crucial aspect of computer science and software engineering.

To learn more about algorithms, visit the Wikipedia page on the subject.

Bubble Sort

Bubble Sort is a simple comparison-based sorting algorithm. It works by repeatedly swapping adjacent elements if they are in the wrong order. This process continues until no more swaps are needed, indicating that the list is sorted. Although it's straightforward and easy to implement, Bubble Sort is not efficient for large datasets compared to other sorting algorithms like Quick Sort or Merge Sort. More info .

Hire Python Developers with Cloudfront Skill for Your Remote Tech Team

Boost Your Technical Excellence With Python JWT Skilled Remote Developers

Hire Expert Python Developers Skilled in Shopify API

Learn Algorithms and Data Structures in Python

Algorithms and data structures are important for most programmers to understand.

We just released a course on the freeCodeCamp YouTube channel that is a beginner-friendly introduction to common data structures (linked lists, stacks, queues, graphs) and algorithms (search, sorting, recursion, dynamic programming) in Python.

This course will help you prepare for coding interviews and assessments. In this course, you will:

• Watch live hands-on coding-focused video tutorials
• Practice coding with cloud Jupyter notebooks
• Solve questions from real programming interviews

Aakash N S teaches this course. He is the co-founder and CEO of Jovian and has created many popular courses about machine learning and programming.

The course is broken up into a series of lessons, assignments, and projects. There are Jupyter Notebook files to go along with each section.

Here is what is covered in the course:

Lesson 1 - Binary Search, Linked Lists and Complexity

• Linear and Binary Search
• Complexity and Big O Notation
• Linked Lists using Python Classes

Assignment 1 - Binary Search Practice

• Understand and solve a problem systematically
• Implement linear search and analyze it
• Optimize the solution using binary search

Lesson 2 - Binary Search Trees, Traversals and Recursion

• Binary trees, traversals, and recursion
• Binary search trees & common operations
• Balanced binary trees and optimizations

Assignment 2 - Hash Tables and Python Dictionaries

• Hash tables from scratch in Python
• Handling collisions using linear probing
• Replicating Python dictionaries

Lesson 3 - Sorting Algorithms and Divide & Conquer

• Bubble sort and Insertion Sort
• Merge sort using Divide & Conquer
• Quicksort and average complexity

Assignment 3 - Divide and Conquer Practice

• Implement polynomial multiplication
• Optimize using divide and conquer
• Analyze time and space complexity

Lesson 4 - Recursion and Dynamic Programming

• Recursion and memoization
• Subsequence and knapsack problems
• Backtracking and pruning

Lesson 5 - Graph Algorithms (BFS, DFS & Shortest Paths)

• Graphs, trees, and adjacency lists
• Breadth-first and depth-first search
• Shortest paths and directed graphs

Project - Step-by-Step Solution to a Programming Problem

• Pick an interesting coding problem
• Solve the problem step-by-step
• Document and present the solution

Lesson 6 - Python Interview Questions, Tips & Advice

• Practice questions and solutions
• Tips for solving coding challenges
• Advice for cracking coding interviews

Watch the course below or on the freeCodeCamp.org YouTube channel (13-hour watch).

I'm a teacher and developer with freeCodeCamp.org. I run the freeCodeCamp.org YouTube channel.

If you read this far, thank the author to show them you care. Say Thanks

Learn to code for free. freeCodeCamp's open source curriculum has helped more than 40,000 people get jobs as developers. Get started

Algorithmic Thinking for Python Beginners

In order to learn how to program, you have to get into the right mindset. Specifically, you need to think about problem solving through the lens of algorithmic thinking. Only then can you tackle Python.

Fortunately, this article tries to help you get in the right mindset by giving you an overview of algorithmic thinking. For the most part, however, we’ll just be talking about how to order a pizza. Enjoy!

Table of Contents

Algorithmic thinking overview, pizza ordering algorithm, what could go wrong, accepting user input, tracking variables, developing pseudocode, exploring abstraction, converting algorithms to python code.

To be honest, I’m probably not the best person to be explaining algorithmic thinking. After all, this is a term that I’ve only ever heard in passing, and I don’t know anyone in the industry that uses it. That said, I think it’s a useful enough idea to talk about as we transition into development.

For the uninitiated, algorithmic thinking is this idea of coming up with steps to solve a problem . Naturally, the product of algorithmic thinking is an algorithm: a sequence of steps someone can follow to solve a problem. Examples of algorithms include cooking recipes, GPS directions, etc.

As you can probably imagine, algorithms are the foundation of computing. In order to solve a problem, we write down steps in a language that the computer can understand. For us, that language is Python. For others, that language could be one of many.

Of course, before we can begin to write code, we need to start thinking like a computer. Fortunately, the rest of this article is dedicated to doing just that. In particular, we’ll take a look at a silly example of an algorithm for ordering a pizza. Then, we’ll get meta and talk about the process of generating and testing an algorithm—at least informally.

Algorithmic Thinking Exercises

While we could talk all day about what algorithmic thinking is and why it’s a useful habit to get into, I find it a lot more interesting to look at some real world examples. Specifically, let’s see if we can construct a set of instructions for a rather mundane task like ordering a pizza. Then, we’ll see if we can poke holes in our algorithm.

Typically, when I teach, I like to have students perform the following exercise: write an algorithm (i.e. a set of instructions) to order a pizza. Naturally, this sort of exercise results in a lot of really interesting revelations. After all, not every student in the class has coded before, so they each bring a unique perspective.

For our purposes, I’ll provide an algorithm. If you’d like to take a moment to come up with your own first, that might be a good idea! Otherwise, we’ll start with this one:

• Lookup the phone number of the pizza place
• Dial the number into a phone
• Wait until someone picks up
• Provide employee with address and credit card
• Place order

These directions seem pretty reasonable, right? If we want a pizza, we just have to pick up a phone, place the order, and pay. Before long, a pizza will be at our door!

Of course, as we’ll see, life is never quite this streamlined. In the next section, we’ll take some time to pick this algorithm apart.

When it comes to putting together an algorithm, it’s helpful to think about what could go wrong at each step. For example, in our pizza ordering algorithm, the very first thing we do is lookup the phone number for the pizza place. Surely, there’s some information missing here, right? What pizza place? What does this lookup process look like? Are we using the internet? What if the internet goes down?

Clearly, we’re being pedantic here. When someone gives you directions for something, they make a lot of assumptions about what you already know. For example, it’s assumed that “pizza place” is a placeholder for whatever pizza place you want. Likewise, the lookup process should be pretty straightforward—although, we’re assuming we can actually call the pizza place in 2020.

That said, when I run this activity in class, I like to help students tighten up their algorithms by playing Devil’s Advocate. In other words, in addition to being pedantic, I also purposefully misinterpret directions that were ambiguous—just like this dad making a sandwich:

In our example, there are lot of fun ways we can misinterpret the directions. For example, there’s nothing specifying that the phone number from step 1 is the same phone number in step 2. As a result, I’d probably enter a random number and watch students look at me in disgust.

Another fun way to break this algorithm would be to purposefully misinterpret step three: wait until someone picks up. If my wife picks up the remote, does that count? People would think I’d lost my mind if I just started reciting my address after an event like that—especially considering that an employee would probably pick up in the middle of my rambling.

All kidding aside, approaching algorithms this way is a great way of figuring out if they’re ready to be shared. After all, if I were to write a recipe, I would probably ask a few people to follow it just to see how the steps are interpreted. That said, we’ll take a look at a more structured way of assessing an algorithm in the following sections.

Unfortunately, computers don’t really have the ability to infer information ; we have to tell them everything. As a result, a better algorithm would have to be a lot more specific. For example, any time we refer to “pizza place”, we should indicate that it’s provided by the person following the directions (i.e. the user). Here’s an updated algorithm with all user-supplied information marked in brackets:

• Lookup the phone number of the [pizza place]
• Dial the number into a [phone]
• Provide employee with [address] and [credit card]
• Place [order]

Here, we’ve called out five explicit pieces of information that the user has to bring to the table to be able to place an order: a pizza place, their address and credit card, a phone, and their order.

One benefit of explicitly marking the information provided by the user is that we now have an algorithm that is somewhat more generic. In other words, we can give this algorithm to various people, and they’ll be able to replace the placeholders with whatever they want.

An alternative to this type of design would be to provide explicit directions in place of all the placeholders. For example, instead of talking about a “pizza place,” we could talk explicitly about Domino’s. Likewise, we’d have to specify this algorithm for a specific person (e.g. Jessie’s Pepperoni Pizza Order to Domino’s) because we need an address and a credit card.

For our purposes, we’ll continue with the more generic design.

While we know what information is being provided by the user, there’s still a problem. How do we know what information is being passed from step to step? Surely, it’s obvious to us, but it wouldn’t be so obvious to a computer.

For example, how does the computer know what number to enter in the phone in step 2? We didn’t explicitly state that it was the same number from step 1. In other words, we need to introduce variables .

To do that, we’ll follow a similar syntax for user data. However, instead of square brackets, we’ll use parentheses:

• Lookup the (phone number) of the [pizza place]
• Dial the (phone number) into a phone
• Wait until an (employee) picks up
• Provide the (employee) with [address] and [credit card]

Now, we’re tracking two variables: the phone number for the pizza place and the employee at the pizza place. This is how we transfer information between each step.

If we now take a look at our algorithm, we can start to see a pattern. All actions are defined using verbs , and all data is defined using nouns . In the next section, we’ll look at taking these ideas and converting them into a simple programming language.

At this point, our algorithm hasn’t really changed that match. All we’ve done is label some key pieces of information as either user input or a variable. That said, I’d argue that that’s pretty much all we need. Now, it’s just a matter of converting what we have into an informal programming language called pseudocode .

In general, there are no real rules around pseudocode. In fact, our original algorithm could be considered pseudocode. However, I find it beneficial to try to develop pseudocode that actually looks like code. That way, it’s a lot easier to convert to software.

In our case, we’ll be using a simple function notation for each of our steps. In other words, we’ll try to convert each verb into a function where the nouns are the input and output. For example, step 1 reads: “lookup the phone number of the pizza place.” As Python-like pseudocode, this might look as follows:

Here, we use a function called `lookup()`python that takes the input of a business and returns a phone number. In this case, we store the phone number in a variable called `phone_number`python.

Now, lookup isn’t defined. In other words, we don’t know how this function is going to lookup the phone number for the pizza place, but we expect it to do its job. With this structure, we can plug in any lookup function that fits our requirements—even one that someone else writes for us.

At any rate, it’s not too hard to convert our entire algorithm to pseudocode:

With our algorithm in code, we can sort of see why certain aspects of our algorithm don’t need to be defined. For instance, the `dial()`python function abstracts the idea of dialing a number into a phone. In other words, we assume that it will work as expected .

That said, even with some of the steps abstracted, there are definitely issues with this algorithm. For instance, we never hang up the phone, so we might want to add a `hang_up()`python function:

Also, there’s a lot of messiness associated with making a call. For example, there’s a bit of conversational back and forth, so we might combine steps 4 and 5 to simplify our algorithm:

In the next section, we’ll take a look at why we might choose to abstract some of these ideas to simplify our algorithm.

As I alluded to a bit earlier, it can sometimes be helpful to purposefully leave out the details of a step. I know previously I made a joke about misinterpreting steps, but programming is complicated. If we focus so hard on the details, we’ll never actually get anything done. In other words, it helps to look at the big picture: what is our goal with this algorithm, and what are the major steps in accomplishing that goal?

Also, while we’re hiding a bit behind abstraction with our current algorithm, that doesn’t stop us from defining any of the underlying functions. For example, we might decided to further explain step 1:

• Turn on [computer]
• Open (browser) on [computer]
• Search for [pizza place] (URL) in (browser)
• Click (URL) of [pizza place] to open (website)
• Find (phone number) on (website)

This can then be turned into it’s own Python-like pseudocode:

Naturally, this process is iterative. In other words, we can outline the high level pieces of the algorithm—like looking up the phone number and placing the order—and further define those steps as needed until we reach the right level of detail.

To me, this makes a lot more sense than diving right down to the lowest level of detail. For instance, we don’t need to explain the entire process of purchasing a phone if the user already has one.

On a side note : it might be fun to create a series of articles this way. Define a high-level guide to doing a mundane task then go way down the rabbit hole writing how-to guides for every little detail .

Being able to organize ideas through abstraction is a key part of algorithmic thinking. If we weren’t able to abstract ideas, life would be a lot more difficult. Imagine trying to throw a ball when all you can think about is the number of degrees of rotation your shoulders and hips need to make reach a target. Yeah, that’s not ideal .

At this point, we’ve covered just about everything I think is worth talking about in terms of this example. In the next, section we’ll talk about how algorithmic thinking relates to code development—specifically in Python.

While I’m sure there’s a way to write a program to order a pizza—in fact, I know Jarvis made a video about that exact subject—it’s not exactly trivial to convert our algorithm to code:

Fortunately, there are tons of problems that are more suited for computing. For example, there are a lot of traditional sort of problems that programming was meant to solve like computation. Thanks to the convenience of a calculator, we can quickly tabulate sales and calculate sales tax.

Today, computing has gotten so abstract that we can actually solve some more interesting problems. For instance, what sort of problems do tools like Facebook, Twitter, and TikTok solve? Now, imagine writing up an a set of algorithms for those tools.

Now that we’ve had a chance to talk about algorithmic thinking, I think we can start getting into Python code. First, however, we’ll take a brief tour through a few Python concepts. Specifically, I want to talk about the interpreter, and what it allows us to do. Then, we’ll broadly talk about different types of data. Eventually, of course, we’ll get to the code!

In the meantime, if you want to support this series, you can head over to my list of ways to grow the site. There, you’ll find fun links to things like my newsletter, Patreon, and YouTube channel.

Alternatively, you can check out some of these related articles:

• What Is a Dictionary in Python?
• How to Approach Python From a Java Perspective?

Likewise, here are some resources from the folks at Amazon (ad):

Of course, you can always keep reading (assuming the next part of the series is out). Otherwise, take care! Thanks for stopping by.

One of my friends has decided to start learning Python, so I decided to create a series for him. This series is a dump of everything I’ve learned about Python since 2017. As someone who taught myself Python, I figured this would appeal to folks like me.

Jeremy Grifski

Jeremy grew up in a small town where he enjoyed playing soccer and video games, practicing taekwondo, and trading Pokémon cards. Once out of the nest, he pursued a Bachelors in Computer Engineering with a minor in Game Design. After college, he spent about two years writing software for a major engineering company. Then, he earned a master's in Computer Science and Engineering. Today, he pursues a PhD in Engineering Education in order to ultimately land a teaching gig. In his spare time, Jeremy enjoys spending time with his wife, playing Overwatch and Phantasy Star Online 2, practicing trombone, watching Penguins hockey, and traveling the world.

Recent Posts

College Students Will Sometimes Have a Lapse in Judgement

I'm writing this article because I recently caught myself writing a somewhat nasty message to a student at the end of a long academic year. I had sent the message, did some reading, and later...

We Have to End Our Cultural Obsession With Grades

With yet another school year in the books, I wanted to take a moment to talk about one of the worst parts of being an educator: grading. More specifically, I want to talk about how grading gets in...

Say "Hello, World!" With Python Easy Max Score: 5 Success Rate: 96.24%

Python if-else easy python (basic) max score: 10 success rate: 89.71%, arithmetic operators easy python (basic) max score: 10 success rate: 97.41%, python: division easy python (basic) max score: 10 success rate: 98.68%, loops easy python (basic) max score: 10 success rate: 98.10%, write a function medium python (basic) max score: 10 success rate: 90.31%, print function easy python (basic) max score: 20 success rate: 97.27%, list comprehensions easy python (basic) max score: 10 success rate: 97.68%, find the runner-up score easy python (basic) max score: 10 success rate: 94.16%, nested lists easy python (basic) max score: 10 success rate: 91.70%, cookie support is required to access hackerrank.

Seems like cookies are disabled on this browser, please enable them to open this website

• Computer Vision
• Problem Solving in Python
• Intro to DS and Algo
• Analysis of Algorithm
• Dictionaries
• Linked Lists
• Doubly Linked Lists
• Circular Singly Linked List
• Circular Doubly Linked List
• Tree/Binary Tree
• Binary Search Tree
• Binary Heap
• Sorting Algorithms
• Searching Algorithms
• Single-Source Shortest Path
• Topological Sort
• Dijkstra’s
• Bellman-Ford’s
• All Pair Shortest Path
• Minimum Spanning Tree
• Kruskal & Prim’s

Problem-solving is the process of identifying a problem, creating an algorithm to solve the given problem, and finally implementing the algorithm to develop a computer program .

An algorithm is a process or set of rules to be followed while performing calculations or other problem-solving operations. It is simply a set of steps to accomplish a certain task.

In this article, we will discuss 5 major steps for efficient problem-solving. These steps are:

• Understanding the Problem
• Exploring Examples
• Breaking the Problem Down
• Solving or Simplification
• Looking back and Refactoring

While understanding the problem, we first need to closely examine the language of the question and then proceed further. The following questions can be helpful while understanding the given problem at hand.

• Can the problem be restated in our own words?
• What are the inputs that are needed for the problem?
• What are the outputs that come from the problem?
• Can the outputs be determined from the inputs? In other words, do we have enough information to solve the given problem?
• What should the important pieces of data be labeled?

Example : Write a function that takes two numbers and returns their sum.

• Implement addition
• Integer, Float, etc.

Once we have understood the given problem, we can look up various examples related to it. The examples should cover all situations that can be encountered while the implementation.

• Start with simple examples.
• Progress to more complex examples.
• Explore examples with empty inputs.
• Explore examples with invalid inputs.

Example : Write a function that takes a string as input and returns the count of each character

After exploring examples related to the problem, we need to break down the given problem. Before implementation, we write out the steps that need to be taken to solve the question.

Once we have laid out the steps to solve the problem, we try to find the solution to the question. If the solution cannot be found, try to simplify the problem instead.

The steps to simplify a problem are as follows:

• Find the core difficulty
• Temporarily ignore the difficulty
• Write a simplified solution
• Then incorporate that difficulty

Since we have completed the implementation of the problem, we now look back at the code and refactor it if required. It is an important step to refactor the code so as to improve efficiency.

The following questions can be helpful while looking back at the code and refactoring:

• Can we check the result?
• Can we derive the result differently?
• Can we understand it at a glance?
• Can we use the result or mehtod for some other problem?
• Can you improve the performance of the solution?
• How do other people solve the problem?

Trending Posts You Might Like

• File Upload / Download with Streamlit
• Seaborn with STREAMLIT
• Dijkstra’s Algorithm in Python
• Greedy Algorithms in Python

Author : Bhavya

• What is Career Pathways?
• Architecture, Manufacturing , Natural Resources and Agriculture
• Arts, Communications, Media and Marketing
• Business, Public Service and Tourism
• Healthcare and Health Sciences
• Human Services, Consulting and Education
• Artificial Intelligence
• Science, Technology, Engineering and Mathematics
• First & Second Year Students
• First Generation Students
• International Students
• Students With Disabilities
• Veteran Students
• Experience Changes Everything
• Featured Jobs
• Featured Internships
• Expand Your Network / Mentor
• Explore Your Interests / Self Assessment
• Job Market Data
• Negotiate an Offer
• Prepare for an Interview
• Prepare for Graduate School
• Search for a Job / Internship
• Academic Job Search Documents
• Create a Resume
• Create a Curriculum Vitae (CV)
• Create a Cover Letter
• Learn about Portfolios
• Learn all about LinkedIn
• Write a Personal Statement
• Professional Communication
• Molm Family Gator Career Career Closet
• Career Fairs
• Career Planning Appointments
• Career Prep Process
• Career Readiness Check-in
• Express Drop-In
• Industry Connections
• What Can I Do With This Major?

Foundations of Algorithmic Thinking with Python

• Share This: Share Foundations of Algorithmic Thinking with Python on Facebook Share Foundations of Algorithmic Thinking with Python on LinkedIn Share Foundations of Algorithmic Thinking with Python on X

Instructor: Robin Andrews

The word “algorithm,” at one time the sole province of mathematics and computer science, has entered the modern vernacular because, for better or worse, algorithms have never been more important or more impactful in daily life. If you’re a developer, you need to be familiar with a wide range of algorithmic thinking in order to be able to solve new problems as they present themselves. If you’re already familiar with Python, becoming more versed in algorithmic thinking is a great way to increase your value as a developer. In this course, Robin Andrews explains how Python, because of its clarity and expressiveness, is the ideal tool for exploring algorithmic thinking. He shows you tools to help you understand the flow of algorithms, explains the brute force approach to solving algorithms, details the concepts of time and space complexity with regard to algorithm analysis, the decrease and conquer strategy, and much more.

• Title Algorithmic Problem Solving with Python
• Author(s) John B. Schneider , Shira Lynn Broschat, Jess Dahmen
• Publisher: Washington State University (February 22, 2019. Updated Continuously)
• License(s): Creative Commons Attribution-ShareAlike 4.0 International License
• Hardcover/Paperback N/A
• eBook PDF Files and a single PDF (360 pages, 2.1 MB)
• Language: English
• ISBN-10: N/A
• ISBN-13: N/A

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.

The book deals with some of the most important and challenging areas of programming and computer science in a highly readable manner. It covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs.

Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others.

If you are a Python lover and looking to learn EVERYTHING you need to learn about the Python approach to algorithm design and analysis, this book is exactly what you need.

• John B. Schneider is an Associate Professor of Electrical Engineering & Computer Science at Washington State University.
• Python Programming
• Algorithms and Data Structures
• Computational Complexity
• Algorithmic Problem Solving with Python (John B. Schneider, et al)
• The Mirror Site (1) - Videos Lectures
• The Mirror Site (2) - PDF
• Book Homepage (Code and Data, Solutions, Video, etc.)

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 is a textbook about computer science. It is also about Python. However, there is much more. The tools and techniques that you learn here will be applied over and over as you continue your study of computer science.

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.

• IT Research Library
• Books by O'Reilly®
• Pro Certificates Studies
• Careers and Job Interviews
• Project Management
• Search Engines
• Developer Tools
• Bargin Computer Books
• Free IT Magazines
• About This Site

Problem Solving and Python Programming - Python Algorithmic Problem Solving: short important questions and answers | Problem Solving and Python Programming : Algorithmic Problem Solving

Chapter: problem solving and python programming : algorithmic problem solving, python algorithmic problem solving: short important questions and answers.

ALGORITHMIC PROBLEM SOLVING

1.   What is an algorithm?

Algorithm is an ordered sequence of finite, well defined, unambiguous instructions for completing a task. It is an English-like representation of the logic which is used to solve the problem. It is a step- by-step procedure for solving a task or a problem. The steps must be ordered, unambiguous and finite in number.

2. Write an algorithm to find minimum of 3 numbers in a list. ALGORITHM : Find Minimum of 3 numbers in a list

Step 1: Start

Step 2: Read the three numbers A, B, C

Step 3: Compare A and B.

If A is minimum, go to step 4 else go to step 5. Step 4: Compare A and C.

If A is minimum, output “A is minimum” else output “C is minimum”. Go to step 6.

Step 5: Compare B and C.

If B is minimum, output “B is minimum” else output “C is minimum”.

Step 6: Stop

3. List the building blocks of an algorithm.

The building blocks of an algorithm are

·                   Statements

·                   Sequence

·                   Selection or Conditional

·                   Repetition or Control flow

·                   Functions

4. Define statement. List its types.

Statements are instructions in Python designed as components for algorithmic problem solving, rather than as one-to-one translations of the underlying machine language instruction set of the computer.

There are three types of high-level programming language statements Input/output statements make up one type of statement. An input statement collects a specific value from the user for a variable within the program. An output statement writes a message or the value of a program variable to the user’s screen.

5. Write the pseudo code to calculate the sum and product of two numbers and display it.

INITIALIZE variables sum, product, number1, number2 of type real

PRINT “Input two numbers”

READ number1, number2

sum = number1 + number2

PRINT “The sum is “, sum

COMPUTE product = number1 * number2

PRINT “The Product is “, product

END program

6. How does flow of control work?

Control flow (or flow of control) is the order in which individual statements, instructions or function calls of an imperative program are executed or evaluated. A control flow statement is a statement in which execution results in a choice being made as to which of two or more paths to follow.

7. What is a function?

Functions are "self-contained" modules of code that accomplish a specific task. Functions usually "take in" data, process it, and "return" a result. Once a function is written, it can be used over and over and over again. Functions can be "called" from the inside of other functions.

8. Write the pseudo code to calculate the sum and product displaying the answer on the monitor screen.

READ number1, number2 sum = number1 + number2

9. Give the rules for writing Pseudo codes.

·  Write one statement per line.

·  Capitalize initial keywords.

·  Indent to show hierarchy.

·  End multiline structure.

·  Keep statements to be language independent.

10. Give the difference between flowchart and pseudo code.

Flowchart and Pseudo code are used to document and represent the algorithm. In other words, an algorithm can be represented using a flowchart or a pseudo code. Flowchart is a graphical representation of the algorithm. Pseudo code is a readable, formally styled English like language representation of the algorithm.

11. Define a flowchart.

A flowchart is a diagrammatic representation of the logic for solving a task. A flowchart is drawn using boxes of different shapes with lines connecting them to show the flow of control. The purpose of drawing a flowchart is to make the logic of the program clearer in a visual form.

12. Give an example of iteration.

for i from 1 to 3            // loop three times

a = a + I            // add the current value of i to a

print a // the number 6 is printed (0 + 1; 1 + 2; 3 + 3)

13. Write down the rules for preparing a flowchart.

While drawing a flowchart, some rules need to be followed—

(1)            A flowchart should have a start and end,

(2)            The direction of flow in a flowchart must be from top to bottom and left to right, and

(3)            The relevant symbols must be used while drawing a flowchart.

14. List the categories of Programming languages.

Programming languages are divided into the following categories:

Interpreted, Functional, Compiled, Procedural, Scripting, Markup, Logic-Based, Concurrent and Object-Oriented Programming Languages

15. Mention the characteristics of an algorithm.

·                   Algorithm should be precise and unambiguous.

·                   Instruction in an algorithm should not be repeated infinitely.

·                   Ensure that the algorithm will ultimately terminate.

·                   Algorithm should be written in sequence.

·                   Algorithm should be written in normal English.

·                   Desired result should be obtained only after the algorithm terminates.

16. Compare machine language, assembly language and high-level language.

Machine  language  is  a  collection  of  binary  digits  or  bits  that  the  computer  reads  and interprets. 

This language is not easily understandable by the human.

An assembly language directly controls the physical hardware. A program written in assembly language consists of a series of instructions mnemonics that correspond to a stream of executable instructions, when translated by an assembler can be loaded into memory and executed. The programs written in this language are not portable and the debugging process is also not very easy.

A high level language is much more abstract, that must be translated or compiled in to machine language. It is easily understandable and the programs are portable. Debugging the code is easy and the program written is not machine dependent.

17. What is the difference between algorithm and pseudo code?

An algorithm is a systematic logical approach used to solve problems in a computer while pseudo code is the statement in plain English that may be translated later to a programming language. Pseudo code is the intermediary between algorithm and program.

18.           List out the simple steps to develop an algorithm.  Algorithm development process consists of five major steps.

Step 1: Obtain a description of the problem.

Step 2: Analyze the problem.

Step 3: Develop a high-level algorithm.

Step 4: Refine the algorithm by adding more detail.

Step 5: Review the algorithm.

19. Give the differences between recursion and iteration.

20. What are advantages and disadvantages of recursion?

1.               What is mean by problem solving?

2.               List down the problem solving techniques?

3.               Define algorithm?

4.               What are the properties of algorithm?

5.               List down the equalities of good algorithm?

6.               Define statements?

7.               Define state?

8.               What is called control flow?

9.               What is called sequence execution?

10.         Define iteration?

11.         What is mean by flow chart?

12.         List down the basic symbols for drawing flowchart?

13.         List down the rules for drawing the flowchart?

14.         What are the advantages of flowchart?

15.         What are the disadvantages of flowchart?

16.         Define pseudo code?

17.         List down the keywords used in writing pseudo code?

18.         Mention the advantages of using pseudo code?

19.         Mention the disadvantages of using pseudo code?

20.         What are the ways available to represent algorithm?

21.         Differentiate flowchart and pseudo code?

22.         Differentiate algorithm and pseudo code?

23.         What is programming language?

24.         Mention the types of programming language?

25.         What is mean by machine level language?

26.         What are the advantages and disadvantages of machine level language?

27.         What is high level programming language and mention its advantages?

28.         What are the steps in algorithmic problem solving?

29.         Write the algorithm for any example?

30.         Draw the flow chart for any example?

31.         Write pseudo code for any example?

Related Topics

Privacy Policy , Terms and Conditions , DMCA Policy and Compliant

Copyright © 2018-2024 BrainKart.com; All Rights Reserved. Developed by Therithal info, Chennai.

Challenge: Greedy Algorithms

Challenge yourself by solving a problem related to greedy algorithms.

• Logic building
• Time complexity
• Coding workspace

Let's practice what we have learned so far.

Create a free account to access the full course.

By signing up, you agree to Educative's Terms of Service and Privacy Policy