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  • 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?

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University of Alberta

Problem Solving, Python Programming, and Video Games

Taught in English

Some content may not be translated

Financial aid available

70,710 already enrolled

Gain insight into a topic and learn the fundamentals

Duane Szafron

Instructors: Duane Szafron +1 more

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Skills you'll gain

  • Python Syntax And Semantics
  • Computer Science
  • Python Programming
  • Problem Solving
  • Video Games

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There are 12 modules in this course

This course is an introduction to computer science and programming in Python. Upon successful completion of this course, you will be able to:

1. Take a new computational problem and solve it, using several problem solving techniques including abstraction and problem decomposition. 2. Follow a design creation process that includes: descriptions, test plans, and algorithms. 3. Code, test, and debug a program in Python, based on your design. Important computer science concepts such as problem solving (computational thinking), problem decomposition, algorithms, abstraction, and software quality are emphasized throughout. This course uses problem-based learning. The Python programming language and video games are used to demonstrate computer science concepts in a concrete and fun manner. The instructional videos present Python using a conceptual framework that can be used to understand any programming language. This framework is based on several general programming language concepts that you will learn during the course including: lexics, syntax, and semantics. Other approaches to programming may be quicker, but are more focused on a single programming language, or on a few of the simplest aspects of programming languages. The approach used in this course may take more time, but you will gain a deeper understanding of programming languages. After completing the course, in addition to learning Python programming, you will be able to apply the knowledge and skills you acquired to: non-game problems, other programming languages, and other computer science courses. You do not need any previous programming, Python, or video game experience. However, several basic skills are needed: computer use (e.g., mouse, keyboard, document editing), elementary mathematics, attention to detail (as with many technical subjects), and a “just give it a try” spirit will be keys to your success. Despite the use of video games for the main programming project, PVG is not about computer games. For each new programming concept, PVG uses non-game examples to provide a basic understanding of computational principles, before applying these programming concepts to video games. The interactive learning objects (ILO) of the course provide automatic, context-specific guidance and feedback, like a virtual teaching assistant, as you develop problem descriptions, functional test plans, and algorithms. The course forums are supported by knowledgeable University of Alberta personnel, to help you succeed. All videos, assessments, and ILOs are available free of charge. There is an optional Coursera certificate available for a fee.

Module 0: Introduction

In Module 0, you will meet the instructional team and be introduced to the four themes of this course: computer science, problem solving, Python programming, and how to create video games.

What's included

6 videos 2 readings 1 quiz

6 videos • Total 31 minutes

  • Course Themes • 7 minutes • Preview module
  • Computer Science • 5 minutes
  • Programming Languages • 3 minutes
  • Learning Outcomes and Problem-Based Learning • 4 minutes
  • How to Get the Most Out of this Course • 5 minutes
  • Suggestions for Learner Success • 5 minutes

2 readings • Total 20 minutes

  • Instructor Bios • 10 minutes
  • Acknowledgements • 10 minutes

1 quiz • Total 12 minutes

  • Introduction • 12 minutes

Module 1: Design Hacking Version 1

In Module 1, you will explore the game creation process that is used in this course. You will use this process to design Version 1 of the first game, Hacking. You will use two problem-solving techniques: problem decomposition and algorithms. You will explore five criteria for problem decomposition: experiential decomposition, feature selection, problem refinement, spatial decomposition, and temporal decomposition. To create your design for Hacking Version 1, you will use three interactive learning objects: the description builder, functional test plan builder, and algorithm builder.

7 videos 9 readings 6 quizzes

7 videos • Total 58 minutes

  • Game Creation Process • 7 minutes • Preview module
  • Observe Hacking Game • 2 minutes
  • Game Versions • 10 minutes
  • Observe Hacking Version 1 • 1 minute
  • Describe Hacking Version 1 • 11 minutes
  • Create Test Plan for Hacking Version 1 • 13 minutes
  • Create Algorithm for Hacking Version 1 • 10 minutes

9 readings • Total 85 minutes

  • The PVG Virtual Machine • 20 minutes
  • Play Hacking Game • 10 minutes
  • Play Hacking Version 1 • 10 minutes
  • Describe Hacking Version 1 • 10 minutes
  • Hacking Version 1 Description Solution • 5 minutes
  • Create Test Plan for Hacking Version 1 • 10 minutes
  • Hacking Version 1 Test Plan Solution • 5 minutes
  • Hacking Version 1 Algorithm Solution • 5 minutes

6 quizzes • Total 158 minutes

  • Game Creation Process • 30 minutes
  • Understand Hacking • 30 minutes
  • Game Versions • 30 minutes
  • Understand Hacking Version 1 • 30 minutes
  • The Game Creation Process • 30 minutes
  • Problem Solving Ontology • 8 minutes

Module 2: Program Hacking Version 1

In Module 2, you will discover how lexics, syntax, and semantics can be used to understand and describe programming languages. You will use these concepts to understand your first Python statement (expression statement), first three Python expressions (literal, identifier, function call), and first five Python types (int, str, float, function, NoneType). You will use these Python constructs to write, test, and debug Hacking Version 1, a text-based game version. You will then reflect on your game version by using a third problem-solving technique called abstraction, including the specific technique of solution generalization, to solve similar problems.

12 videos 7 readings 11 quizzes

12 videos • Total 79 minutes

  • Python Evaluation Examples • 5 minutes • Preview module
  • Python Interpretation • 10 minutes
  • Python Lexical Analysis • 7 minutes
  • Python Syntax Analysis • 11 minutes
  • Python Objects • 5 minutes
  • Python Semantics of Literals and Identifiers • 10 minutes
  • Python Semantics of Function Calls • 3 minutes
  • Python Program Interpretation • 5 minutes
  • Program Hacking Version 1 • 9 minutes
  • The Reflection Process • 2 minutes
  • Review Code for Hacking Version 1 • 5 minutes
  • Solution Issues • 2 minutes

7 readings • Total 80 minutes

  • The Python Shell in the Wing IDE • 20 minutes
  • Lexical Rules, Tables and Sample Problem (identifier, literal and delimiter) • 10 minutes
  • Syntax Diagrams and Sample Problem (expression statement, expression and function call) • 10 minutes
  • Semantic Rules and Sample Problem (identifier, literal and function call) • 10 minutes
  • Programming With the Wing IDE • 10 minutes
  • Hacking Version 1 Solution Code • 10 minutes
  • Software Quality Tests for Hacking Version 1 • 10 minutes

11 quizzes • Total 154 minutes

  • Python Evaluation Examples • 5 minutes
  • Interpretation (lexical analysis, syntax analysis and semantic analysis) • 6 minutes
  • Lexical Analysis (identifier, literal and delimiter) • 15 minutes
  • Syntax Analysis (expression statement, expression and function call) • 12 minutes
  • Python Objects • 6 minutes
  • Semantic Analysis (identifier, literal and function call) • 6 minutes
  • Evaluation (identifier, literal and function call) • 30 minutes
  • Programming (identifier, literal and function call) • 30 minutes
  • Program Hacking Version 1 • 30 minutes
  • Reflect on Language Concepts used in Hacking Version 1 • 9 minutes
  • The Game Creation Process • 5 minutes

Module 3: Hacking Version 2

In Module 3, you will identify solution issues in your game. You will apply a second form of the abstraction problem-solving technique, called using templates, to solve a solution issue by using a graphics library. You will then use lexics, syntax, and semantics to learn two new Python statements (assignment, import), two new Python expressions (binary expression, attribute reference), and one new Python type (module). You will employ these Python constructs and a simple graphics library to write, test, and debug Hacking Version 2.

12 videos 21 readings 30 quizzes

12 videos • Total 73 minutes

  • Solution Issues in Hacking Version 1 • 5 minutes • Preview module
  • Observe Hacking Version 2 • 1 minute
  • Describe Hacking Version 2 • 5 minutes
  • Regression Testing and Deleting Obsolete Tests • 4 minutes
  • Create Algorithm for Hacking Version 2 • 1 minute
  • Python Assignment Statement • 8 minutes
  • Python Binary Expression and Operator Token • 8 minutes
  • Python Import Statement and Keyword Token • 7 minutes
  • Python Multi-argument Function Call • 5 minutes
  • Python Method Call and Attribute Reference • 7 minutes
  • Program Hacking Version 2 • 11 minutes
  • Review Code for Hacking Version 2 • 5 minutes

21 readings • Total 195 minutes

  • Play Hacking Version 2 • 10 minutes
  • Describe Hacking Version 2 • 10 minutes
  • Hacking Version 2 Description Solution • 5 minutes
  • Create Test Plan for Hacking Version 2 • 10 minutes
  • Hacking Version 2 Test Plan Solution • 5 minutes
  • Create Algorithm for Hacking Version 2 • 10 minutes
  • Hacking Version 2 Algorithm • 5 minutes
  • Syntax Diagrams and Sample Problem (statement and assignment statement ) • 10 minutes
  • Semantic Rules (assignment statement) • 10 minutes
  • Lexical Rules and Tables (operator) • 10 minutes
  • Syntax Diagrams and Sample Problem (binary expression and binary operator) • 10 minutes
  • Semantic Rules and Sample Problem(binary expression) • 10 minutes
  • Lexical Rules and Tables (keyword) • 10 minutes
  • Syntax Diagrams and Sample Problem (import statement and module) • 10 minutes
  • Semantic Rules and Sample Problem (import statement) • 10 minutes
  • Syntax Diagrams and Sample Problem (multi-argument function call) • 10 minutes
  • Semantic Rules and Sample Problem (multi-argument function call) • 10 minutes
  • Syntax Diagrams and Sample Problem(method call and attribute reference) • 10 minutes
  • Semantic Rules and Sample Problem (method call and attribute reference) • 10 minutes
  • Hacking Version 2 Solution Code • 10 minutes
  • Software Quality Tests for Hacking Version 2 • 10 minutes

30 quizzes • Total 516 minutes

  • Syntax Analysis (import statement and module) • 9 minutes
  • Semantic Analysis (import statement) • 8 minutes
  • Syntax Analysis (multi-argument function call) • 27 minutes
  • Semantic Analysis (multi-argument function call) • 21 minutes
  • Syntax Analysis (method call and attribute reference) • 21 minutes
  • Semantic Analysis (method call and attribute reference) • 12 minutes
  • Solution Issues in Hacking Version 1 • 6 minutes
  • Understand Hacking Version 2 • 5 minutes
  • Delete Obsolete Tests for Hacking Version 2 • 5 minutes
  • Lexical Analysis (review) • 30 minutes
  • Syntax Analysis (statement and assignment statement) • 15 minutes
  • Semantic Analysis (assignment statement) • 9 minutes
  • Evaluation (assignment statement) • 11 minutes
  • Programming (assignment statement) • 30 minutes
  • Lexical Analysis (operator) • 15 minutes
  • Syntax Analysis (binary expression and binary operator) • 27 minutes
  • Semantic Analysis (binary expression) • 14 minutes
  • Evaluation (binary expression and operator) • 14 minutes
  • Programming (binary expression and operator) • 30 minutes
  • Lexical Analysis (keyword) • 14 minutes
  • Evaluation (import statement and keyword) • 10 minutes
  • Programming (import statement and keyword) • 30 minutes
  • Lexical Analysis (review) • 17 minutes
  • Evaluation (multi-argument function call) • 11 minutes
  • Programming (multi-argument function call) • 30 minutes
  • Lexical Analysis (review) • 14 minutes
  • Evaluation (method call and attribute reference) • 9 minutes
  • Programming (method call and attribute reference) • 30 minutes
  • Program Hacking Version 2 • 30 minutes
  • Reflect on language concepts used in Hacking Version 2 • 12 minutes

Module 4: Hacking Version 3

In Module 4, you will modify your game design to support multiple gameplay paths using a new problem decomposition criteria called case-based decomposition, which utilizes a selection control structure. You will learn one new Python statement (if), one new Python expression (unary expression), and one new Python type (bool). You will employ these Python constructs to write, test, and debug Hacking Version 3.

8 videos 16 readings 19 quizzes

8 videos • Total 39 minutes

  • Solution Issues in Hacking Version 2 • 2 minutes • Preview module
  • Observe Hacking Version 3 • 0 minutes
  • Create Algorithm for Hacking Version 3 • 4 minutes
  • Python If Statement and Boolean Type • 10 minutes
  • Python Elif and Else Clauses • 7 minutes
  • Python Keyword Operator, Short Circuit Evaluation, Unary Expression, and Operator Precedence • 7 minutes
  • Program Hacking Version 3 • 2 minutes
  • Review Code for Hacking Version 3 • 4 minutes

16 readings • Total 145 minutes

  • Play Hacking Version 3 • 10 minutes
  • Describe Hacking Version 3 • 10 minutes
  • Hacking Version 3 Description Solution • 5 minutes
  • Create Test Plan for Hacking Version 3 • 10 minutes
  • Hacking Version 3 Test Plan Solution • 5 minutes
  • Create Algorithm for Hacking Version 3 • 10 minutes
  • Hacking Version 3 Algorithm Solution • 5 minutes
  • Lexical Rules, Tables and Sample Problem (newline, indent and dedent) • 10 minutes
  • Syntax Diagrams (if statement) • 10 minutes
  • Semantic Rules and Sample Problem (if statement) • 10 minutes
  • Syntax Diagrams and Sample Problem (elif and else clause) • 10 minutes
  • Semantic Rules and Sample Problem (elif and else clauses) • 10 minutes
  • Syntax Diagrams and Sample Problem (keyword operator, unary expression, unary operator, and operator precedence) • 10 minutes
  • Semantic Rule and Sample Problem (keyword operator and unary expression) • 10 minutes
  • Hacking Version 3 Solution Code • 10 minutes
  • Software Quality Tests in Hacking Version 3 • 10 minutes

19 quizzes • Total 479 minutes

  • Evaluation (if statement and boolean) • 14 minutes
  • Lexical Analysis (review) • 33 minutes
  • Syntax Analysis (elif and else clauses) • 54 minutes
  • Semantic Analysis (elif and else clause) • 30 minutes
  • Evaluation (elif and else clauses) • 16 minutes
  • Lexical Analysis (review) • 32 minutes
  • Semantic Analysis (short circuit evaluation and operator precedence) • 30 minutes
  • Reflect on language concepts used in Hacking Version 3 • 8 minutes
  • Solution Issues in Hacking Version 2 • 5 minutes
  • Understand Hacking Version 3 • 5 minutes
  • Delete Obsolete Tests for Hacking Version 3 • 5 minutes
  • Lexical Analysis (newline, indent and dedent) • 26 minutes
  • Syntax Analysis (if statement) • 33 minutes
  • Semantic Analysis (if statement) • 23 minutes
  • Programming (if statement and boolean type) • 30 minutes
  • Programming (elif and else clause) • 30 minutes
  • Syntax Analysis (keyword operator, unary expression, unary operator, and operator precedence) • 45 minutes
  • Programming (keyword operator, short circuit evaluation, unary expression, and operator precedence) • 30 minutes
  • Program Hacking Version 3 • 30 minutes

Module 5: Hacking Version 4 & 5

In Module 5, you will modify your game design using two new abstraction techniques, called control abstraction and data abstraction. You will explore two different control abstractions, called definite and indefinite repetition. You will learn two new Python statements (for, while), four new Python expressions (subscription expression, expression list, parenthesized expression, list display), and three new Python types (tuple, list, range). You will employ these Python constructs to write, test, and debug Hacking Version 4 and Hacking Version 5.

17 videos 22 readings 29 quizzes

17 videos • Total 73 minutes

  • Solution Issues in Hacking Version 3 • 4 minutes • Preview module
  • Create Algorithm for Hacking Version 4 • 4 minutes
  • Python Sequences and Subscription • 8 minutes
  • Python Tuple and List Types • 5 minutes
  • Python Sequence Element Replacement • 4 minutes
  • Python For Statement • 5 minutes
  • Program Hacking Version 4 • 2 minutes
  • Review Code for Hacking Version 4 • 5 minutes
  • Solution Issues in Hacking Version 4 • 3 minutes
  • Observe Hacking Version 5 • 1 minute
  • Describe Hacking Version 5 • 0 minutes
  • Create Test Plan for Hacking Version 5 • 2 minutes
  • Create Algorithm for Hacking Version 5 • 4 minutes
  • Python While Statement • 4 minutes
  • Python Repetition Examples and Range Type • 8 minutes
  • Program Hacking Version 5 • 1 minute
  • Review Code for Hacking Version 5 • 4 minutes

22 readings • Total 200 minutes

  • Create Algorithm for Hacking Version 4 • 10 minutes
  • Hacking Version 4 Algorithm Solution • 5 minutes
  • Syntax Diagrams and Sample Problem (subscription) • 10 minutes
  • Semantic Rules and Sample Problem (subscription) • 10 minutes
  • Syntax Diagrams and Sample Problem (expression list, parenthesized expression, list display) • 10 minutes
  • Semantic Rules and Sample Problem (tuple and list type) • 10 minutes
  • Syntax Diagrams and Sample Problem (assignment target: subscription) • 10 minutes
  • Semantic Rules and Sample Problem (assignment target: subscription) • 10 minutes
  • Syntax Diagrams and Sample Problem (for statement) • 10 minutes
  • Semantic Rules and Sample Problem (for statement) • 10 minutes
  • Hacking Version 4 Solution Code • 10 minutes
  • Software Quality Tests for Hacking Version 4 • 10 minutes
  • Play Hacking Version 5 • 10 minutes
  • Describe Hacking Version 5 • 10 minutes
  • Hacking Version 5 Description Solution • 5 minutes
  • Create Test Plan for Hacking Version 5 • 10 minutes
  • Hacking Version 5 Test Plan Solution • 5 minutes
  • Create Algorithm for Hacking Version 5 • 10 minutes
  • Hacking Version 5 Algorithm Solution • 5 minutes
  • Syntax Diagrams and Sample Problem (while statement) • 10 minutes
  • Semantic Analysis and Sample Problem (while statement) • 10 minutes
  • Hacking Version 5 Solution Code • 10 minutes

29 quizzes • Total 644 minutes

  • Lexical Analysis (review) • 35 minutes
  • Evaluation (sequences and subscription) • 30 minutes
  • Syntax Analysis (expression list, parenthesized expression and list display) • 33 minutes
  • Semantic Analysis (tuple and list type) • 12 minutes
  • Evaluation (tuple and list type) • 12 minutes
  • Syntax Analysis (assignment target: subscription) • 24 minutes
  • Semantic Analysis (assignment target: subscription) • 20 minutes
  • Evaluation (sequence element replacement) • 8 minutes
  • Syntax Analysis (for statement) • 33 minutes
  • Evaluation (for statement) • 8 minutes
  • Reflect on Language Concepts used in Hacking Version 4 • 6 minutes
  • Syntax Analysis (while statement) • 30 minutes
  • Semantic Analysis (while statement) • 26 minutes
  • Evaluation (while statement) • 8 minutes
  • Evaluation (range type) • 8 minutes
  • Reflect on Language Concepts used in Hacking Version 5 • 11 minutes
  • Syntax Analysis (subscription) • 33 minutes
  • Semantic Analysis (subscription) • 30 minutes
  • Programming (sequences and subscription) • 30 minutes
  • Programming (tuple and list types) • 30 minutes
  • Programming (sequence element replacement) • 30 minutes
  • Semantic Analysis (for statement) • 29 minutes
  • Programming (for statement) • 30 minutes
  • Program Hacking Version 4 • 30 minutes
  • Understand Hacking Version 5 • 5 minutes
  • Delete Obsolete Descriptions from Hacking Version 5 • 3 minutes
  • Programming (while statement) • 30 minutes
  • Programming (range type) • 30 minutes
  • Program Hacking Version 5 • 30 minutes

Module 6: Hacking Version 6

In Module 6, you will learn a new control abstraction called a user-defined function. You will learn how to implement user-defined functions using two new Python statements (function definition, return). You will employ these Python constructs to significantly improve the quality of your code in Hacking Version 6.

8 videos 8 readings 19 quizzes

8 videos • Total 63 minutes

  • Solution Issues in Hacking Version 5 • 3 minutes • Preview module
  • Python Function Definitions • 15 minutes
  • Python Function Parameters • 6 minutes
  • Python Main Function and Identifier Scope • 9 minutes
  • Python Return Statement • 5 minutes
  • Python Side Effects • 6 minutes
  • Program Hacking Version 6 • 9 minutes
  • Review Code for Hacking Version 6 • 8 minutes

8 readings • Total 80 minutes

  • Syntax Diagrams and Sample Problem(function definition) • 10 minutes
  • Semantic Rules and Sample Problem (function definition) • 10 minutes
  • Syntax Diagrams (parameter list) • 10 minutes
  • Semantic Rules and Sample Problem (parameter list) • 10 minutes
  • Syntax Diagrams and Sample Problem (return statement) • 10 minutes
  • Semantic Rules and Sample Problem (return statement) • 10 minutes
  • Hacking Version 6 Solution Code • 10 minutes
  • Software Quality Tests for Hacking Version 6 • 10 minutes

19 quizzes • Total 434 minutes

  • Lexical Analysis (review) • 29 minutes
  • Syntax Analysis (function definition) • 42 minutes
  • Evaluation (function definition) • 8 minutes
  • Syntax Analysis (parameter list) • 33 minutes
  • Semantic Analysis (parameter list) • 29 minutes
  • Evaluation (function parameters) • 8 minutes
  • Evaluation (main function) • 12 minutes
  • Syntax Analysis (return statement) • 27 minutes
  • Evaluation (return statement) • 12 minutes
  • Evaluation (side effects) • 8 minutes
  • Reflect on Language Concepts used in Hacking Version 6 • 11 minutes
  • Semantic Analysis (function definition) • 18 minutes
  • Programming (function definition) • 30 minutes
  • Programming (function parameters) • 30 minutes
  • Programming (main function) • 30 minutes
  • Semantic Analysis (return statement) • 17 minutes
  • Programming (return statement) • 30 minutes
  • Programming (side effects) • 30 minutes
  • Program Hacking Version 6 • 30 minutes

Module 7: Hacking Version 7

In Module 7, you will not learn any new problem-solving techniques or Python language features. Instead you will exercise your problem-solving skills and practice the language constructs you already know to improve your proficiency. You will add some fun features to the Hacking game by designing, coding, testing, and debugging Hacking Version 7.

5 videos 8 readings 2 quizzes

5 videos • Total 12 minutes

  • Solution Issues in Hacking Version 6 • 1 minute • Preview module
  • Observe Hacking Version 7 • 1 minute
  • Create Algorithm for Hacking Version 7 • 3 minutes
  • Program Hacking Version 7 • 3 minutes
  • Identify Solution Issues in Hacking Version 7 • 2 minutes

8 readings • Total 65 minutes

  • Play Hacking Version 7 • 10 minutes
  • Describe Hacking Version 7 • 10 minutes
  • Hacking Version 7 Description Solution • 5 minutes
  • Create Test Plan for Hacking Version 7 • 10 minutes
  • Hacking Version 7 Test Plan Solution • 5 minutes
  • Create Algorithm for Hacking Version 7 • 10 minutes
  • Hacking Version 7 Algorithm Solution • 5 minutes
  • Hacking Version 7 Solution Code • 10 minutes

2 quizzes • Total 35 minutes

  • Understand Hacking Version 7 • 5 minutes
  • Program Hacking Version 7 • 30 minutes

Module 8: Poke the Dots Version 1 & 2

In Module 8, you will design and implement Version 1 of a new graphical game called Poke the Dots. You will then modify your game design using data abstraction to create user-defined classes. You will learn two new Python statements (class definition, pass) that will allow you to construct your own Python types. You will employ these Python constructs to implement Poke the Dots Version 2.

12 videos 21 readings 17 quizzes

12 videos • Total 83 minutes

  • Introduction to Poke the Dots • 4 minutes • Preview module
  • Observe Poke the Dots Version 1 • 0 minutes
  • Create Algorithm for Poke the Dots Version 1 • 12 minutes
  • Python Import Statement Variations • 9 minutes
  • Python Pass Statement • 2 minutes
  • Program Poke the Dots Version 1 • 13 minutes
  • Review Code for Poke the Dots Version 1 • 9 minutes
  • Solution Issues in Poke the Dots Version 1 • 2 minutes
  • Create Algorithm for Poke the Dots Version 2 • 2 minutes
  • Python Class Definition • 15 minutes
  • Program Poke the Dots Version 2 • 5 minutes
  • Review Code for Poke the Dots Version 2 • 7 minutes

21 readings • Total 185 minutes

  • Play Poke the Dots • 10 minutes
  • Play Poke the Dots Version 1 • 10 minutes
  • Describe Poke the Dots Version 1 • 10 minutes
  • Poke the Dots Version 1 Description Solution • 5 minutes
  • Create Test Plan for Poke the Dots Version 1 • 10 minutes
  • Poke the Dots Version 1 Test Plan Solution • 5 minutes
  • Create Algorithm for Poke the Dots Version 1 • 10 minutes
  • Poke the Dots Version 1 Algorithm Solution • 5 minutes
  • Syntax Diagrams and Sample Problem(import statement variations) • 10 minutes
  • Semantic Rules and Sample Problem (import statement variations) • 10 minutes
  • Syntax Diagrams and Sample Problem(pass statement) • 10 minutes
  • Semantic Rules and Sample Problem (pass statement) • 10 minutes
  • Poke the Dots Version 1 Solution Code • 10 minutes
  • Updated Algorithm for Poke the Dots Version 1 • 10 minutes
  • Poke the Dots Version 1 Updated Algorithm Solution • 5 minutes
  • Create Algorithm for Poke the Dots Version 2 • 10 minutes
  • Poke the Dots Version 2 Algorithm Solution • 5 minutes
  • Syntax Diagrams and Sample Problem (class definition) • 10 minutes
  • Semantic Rules and Sample Problem (class definition) • 10 minutes
  • Poke the Dots Version 2 Solution Code • 10 minutes
  • Software Quality Tests for Poke the Dots Version 2 • 10 minutes

17 quizzes • Total 351 minutes

  • Lexical Analysis (review) • 38 minutes
  • Syntax Analysis (import statement variations) • 18 minutes
  • Semantic Analysis (import statement variations) • 12 minutes
  • Programming (import statement variations) • 30 minutes
  • Syntax Analysis (pass statement) • 24 minutes
  • Semantic Analysis (pass statement) • 21 minutes
  • Programming (pass statement) • 30 minutes
  • Reflect on Language Concepts used in Poke the Dots Version 1 • 6 minutes
  • Syntax Analysis (class definition) • 21 minutes
  • Syntax Analysis (assignment target: attribute reference) • 21 minutes
  • Semantic Analysis (class definition) • 24 minutes
  • Programming (class definition) • 30 minutes
  • Reflect on Language Concepts used in Poke the Dots Version 2 • 6 minutes
  • Understand Poke the Dots • 5 minutes
  • Understand Poke the Dots Version 1 • 5 minutes
  • Program Poke the Dots Version 1 • 30 minutes
  • Program Poke the Dots Version 2 • 30 minutes

Module 9: Poke the Dots Version 3

In Module 9, you will not learn any new problem-solving techniques or Python language features. Instead you will exercise your problem-solving skills and practice the language constructs you already know to improve your proficiency. You will add some fun features to the Poke the Dots game by designing, coding, testing, and debugging Poke the Dots Version 3.

5 videos 8 readings 5 quizzes

5 videos • Total 9 minutes

  • Solution Issues in Poke the Dots Version 2 • 1 minute • Preview module
  • Observe Poke the Dots Version 3 • 1 minute
  • Create Algorithm for Poke the Dots Version 3 • 1 minute
  • Program Poke the Dots Version 3 • 1 minute
  • Review Code for Poke the Dots Version 3 • 4 minutes
  • Play Poke the Dots Version 3 • 10 minutes
  • Describe Poke the Dots Version 3 • 10 minutes
  • Poke the Dots Version 3 Description Solution • 5 minutes
  • Create Test Plan for Poke the Dots Version 3 • 10 minutes
  • Poke the Dots Version 3 Test Plan Solution • 5 minutes
  • Create Algorithm for Poke the Dots Version 3 • 10 minutes
  • Poke the Dots Version 3 Algorithm Solution • 5 minutes
  • Poke the Dots Version 3 Solution Code • 10 minutes

5 quizzes • Total 65 minutes

  • Reflect on Event Categories Used in Poke the Dots Version 3 • 20 minutes
  • Understand Poke the Dots Version 3 • 5 minutes
  • Delete Obsolete Descriptions for Poke the Dots Version 3 • 5 minutes
  • Delete Obsolete Tests for Poke the Dots Version 3 • 5 minutes
  • Program Poke the Dots Version 3 • 30 minutes

Module 10: Poke the Dots Version 4

In Module 10, you will modify your game design using a new form of control abstraction called user-defined methods. User-defined methods allow you to restrict access to the attributes of a class to improve data abstraction. You will employ user-defined methods to implement Poke the Dots Version 4.

6 videos 5 readings 5 quizzes

6 videos • Total 38 minutes

  • Solution Issues in Poke the Dots Version 3 • 4 minutes • Preview module
  • Create Algorithm for Poke the Dots Version 4 • 5 minutes
  • Python User-defined Methods and Self • 11 minutes
  • Python Private Attributes • 3 minutes
  • Program Poke the Dots Version 4 • 6 minutes
  • Review Code for Poke the Dots Version 4 • 7 minutes

5 readings • Total 45 minutes

  • Create Algorithm for Poke the Dots Version 4 • 10 minutes
  • Poke the Dots Version 4 Algorithm Solution • 5 minutes
  • Semantic Rules and Sample Problem (user-defined methods) • 10 minutes
  • Poke the Dots Version 4 Solution Code • 10 minutes
  • Software Quality Tests for Poke the Dots Version 4 • 10 minutes

5 quizzes • Total 128 minutes

  • Programming (user-defined methods) • 30 minutes
  • Reflect on Language Concepts used in Poke the Dots Version 4 • 8 minutes
  • Semantic Analysis (user-defined methods) • 30 minutes
  • Program Poke the Dots Version 4 • 30 minutes

Module 11: Poke the Dots Version 5

In Module 11, you will not learn any new problem-solving techniques or Python language features. Instead you will exercise your problem-solving skills and practice the language constructs you already know to improve your proficiency. You will add some fun features to the Poke the Dots game by designing, coding, testing, and debugging Poke the Dots Version 5.

5 videos • Total 8 minutes

  • Solution Issues in Poke the Dots Version 4 • 0 minutes • Preview module
  • Observe Poke the Dots Version 5 • 1 minute
  • Create Algorithm for Poke the Dots Version 5 • 0 minutes
  • Program Poke the Dots Version 5 • 1 minute
  • Solution Issues in Poke the Dots Version 5 • 4 minutes
  • Play Poke the Dots Version 5 • 10 minutes
  • Describe Poke the Dots Version 5 • 10 minutes
  • Poke the Dots Version 5 Description Solution • 5 minutes
  • Create Test Plan for Poke the Dots Version 5 • 10 minutes
  • Poke the Dots Version 5 Test Plan Solution • 5 minutes
  • Create Algorithm for Poke the Dots Version 5 • 10 minutes
  • Poke the Dots Version 5 Algorithm Solution • 5 minutes
  • Poke the Dots Version 5 Solution Code • 10 minutes
  • Understand Poke the Dots Version 5 • 5 minutes
  • Program Poke the Dots Version 5 • 30 minutes

what is problem solving in python

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Problem Solving with Algorithms and Data Structures using Python ¶

PythonDS Cover

By Brad Miller and David Ranum, Luther College

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

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

Acknowledgements ¶

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

Indices and tables ¶

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Python Programming – Top-Down Approach Of Problem Solving

Python Programming - Top-Down Approach Of Problem Solving

You can learn about Functions in Python  Programs with Outputs helped you to understand the language better.

Top-down design is the technique of breaking down a problem into various major tasks needed to be performed. Each of these tasks is further broken down into separate subtasks, and so on till each sub-task is sufficiently simple to be written as a self-contained or procedure module. The entire solution to the problem will then consist of a series of simple modules. In top-down design, you initially describe the problem you are working on at the highest or most general level. The description of the problem at this level will usually be concerned with what must be done – not how it must be done.

  • Introduction to Python Programming – The Basic Model Of Computation
  • Introduction to Python Programming – Programming Languages
  • Introduction to Python Programming – Documentation

The description will be in terms of complex, higher-level operations. You must take all of the operations at this level and individually break them down into simpler steps that begin to describe how to accomplish the tasks. If these simple steps can be represented as acceptable algorithmic steps, you need not split them any further. If that is not the case, then you split each of these second-level operations individually into still simpler steps. This stepwise refinement continues until each of the original top-level operations has been described in terms of acceptable shortest (primitive) statements.

  • The top-down approach is used in the system analysis and design process.

The top-down approach, starting at the general levels to gain an understanding of the system and gradually moving down to levels of greater detail is done in the analysis stage. In the process of moving from top to bottom, each component is exploded into more and more details. Thus, the problem at hand is analyzed or broken down into major components, each of which is again broken down if necessary.

  • The top-down process involves working from the most general down to the most specific.

The design of modules is reflected in hierarchy charts such as the one shown in Figure 10.1. The purpose of procedure Main is to coordinate the three branch operations e.g. Get, Process, and Put routines. These three routines communicate only through Main. Similarly, Sub1 and Sub2 can communicate only through the Process routine.

Python Programming - Top-Down Approach Of Problem Solving chapter 10 img 1

  • Using the top-down approach, attention is first focussed on global aspects of the overall system. As the design progress takes place, the system is decomposed into subsystems and more consideration is given to specific issues.

Advantages of Top-down Approach

The advantages of the top-down approach are as follows:

a. This approach allows a programmer to remain “on top of” a problem and view the developing solution in context. The solution always proceeds from the highest level downwards. With other techniques, you may find yourselves bogged down with very low-level decisions at a very early stage. It will be difficult to make these deci¬sions if it is not clear as to how they may affect the overall solution of the problem.

b. This would be a very good way to delay deci¬sions on problems whose solution may not be readily available. At each stage in the development, the individual operation will be split up into a number of more elementary steps. If you are not sure how to proceed with Step 1 you can still work on Step 2.

c. By dividing the problem into a number of sub-problems, it is easier to share problem development. For example, one person may solve one part of the problem and the other person may solve another part of the problem.

d. Since debugging time grows quickly when the program is longer, it will be to our advantage to debug a long program divided into a number of smaller segments or parts rather than one big chunk. The top-down development process specifies a solution in terms of a group of smaller, individual subtasks. These subtasks thus become the ideal units of the program for testing and debugging. If you add a new piece of code, say “p” to the overall program “P”, and an error condition occurs, you can definitely state that the error must either be in “p” itself or in the interface between “p” or “P” because “P” has been previously checked and certified.

By testing the program in small pieces, you greatly simplify the debugging process. In addition, you will have the satisfaction of knowing that everything you have coded so far is correct.

e. Another advantage of the top-down development process is that it becomes an ideal structure for managing and implementing a program using a team of programmers. A senior programmer can be made responsible for the design of a high-level task and its decomposition into subtasks. Each of those subtasks can then be “given out” to a junior programmer who works under the direction of the senior staff. Since almost all software projects are done by teams of two or more programmers, this top-down characteristic helps in faster completion of the system.

In summary, the top-down method is a program design technique that analyses a problem in terms of more elementary subtasks. Through the technique of stepwise splitting, you expand and define each of the separate subtasks until the problem is solved. Each subtask is tested and verified before it is expanded further. Thus, there are additional advantages:

a. Increased comprehension of the problem. b. Unnecessary lower-level detail is removed. c. Reduced debugging time.

Bottom-up Approach of Problem Solving

When faced with a large and complex problem, it may be difficult to see how the whole thing can be done. It may be easier to solve parts of the problem individually, taking the easier aspects first and thereby gaining the insight and experience to tackle the more difficult tasks, and finally to try and join them all together to form the complete solution. This is called a bottom-up approach.

This approach suffers from the disadvantage that the parts of the program may not fit together easily. There may be a lack of consistency between mod¬ules, and considerable re-programming may have to be carried out. Hence, this approach is not much favored.

Docstrings serve as documentation for your function to describe what the function does in the pro¬gram so that anyone who reads the function’s docstring understands what your function does, without having to go through all the code in the function definition. These are optional constructs enclosed by triple quotes. It is a comment in the programming language.

Function Parameters and Arguments

Parameters are the value(s) provided in the parenthesis when you write the function header. You can add as many parameters as you want, just separate them with a comma.

  • In other words, the parameter is a name used inside a function to refer to the value passed as an argument.

Arguments are the value(s) provided in function call/invoke statement. This value is assigned to the corresponding parameter in the function. The vari¬ables you write after your function name in def statements are often called the formal parameters of the function. The values you supply when you call the function are called the actual parameters, or argu¬ments. By default, parameters have a positional behavior and you need to inform them in the same order that they were defined. It is common to say that the function takes arguments and returns the result. The return statement can contain an expression that gets evaluated and the value is returned. If there is no expression in the statement or the return statement itself is not present inside a function, then the function will return the None object.

  • The parameter passed by the client is known as the actual parameter. The parameter specified by the function is called the formal parameter.
  • The argument is a value provided to a function when the function is called. This value is assigned to the corresponding parameter in the function.

Parameters are placeholders for information that you give to a function so it can carry out an action. For example, if a function is defined as follows: def display(message): then, the message is a parameter or formal argument. Although display has only one parameter, the function can have many. To define functions with multiple parameters, list them out, separated by commas. In the following statement, It will display a message for you is an argument or actual argument, display(“It will display message for you”) Here are a few things to know about function parameters:

(a) A function can have any number of parameters (or no parameters). (b) When you define a function, you specify how many parameters it has. (c) You can define default values for any or all of the parameters. Here are a few things to know about arguments: (a) Arguments are passed when you call the function. (b) If the parameter has a default value, you don’t have to pass an argument. (c) You can pass an argument as a literal or as a name.

  • The values being passed through a function-call statement are called arguments or actual arguments. The values received in the function definition or header are called parameters or formal parameters.

In Example 4 shown above i.e. def power(x, n): x and n are known as parameters or formal parame¬ters. Whereas in PW = power(a, b) a and b are known as arguments or actual arguments. Arguments in Python can be literals, variables, or expressions.

  • Function header cannot contain expressions.

Anonymous functions

You can use the lambda keyword to create small anonymous functions. These functions are called anonymous because they are not declared in the standard manner by using the def keyword. (a) Lambda forms can take any number of argu¬ments but return just one value in the form of an expression. They cannot contain commands or multiple expressions. (b) An anonymous function cannot be a direct call to print because lambda requires an expression. (c) Lambda functions have their own local name¬space and cannot access variables other than those in their parameter list and those in the global namespace. (d) Although it appears that lambdas are a one-line version of a function, they are not equivalent to inline statements in C or C++, whose purpose is bypassing function stack allocation during invocation for performance reasons. Syntax: The syntax of lambda functions contains only a single statement, which is as follows: lambda [argl [,arg2, argn]]:expression Following is the example to show how lambda form of function works:

Example Demo of Anonymous function.

The return STATEMENT

A Python function could also optionally return a value. This value could be a result produced from your function’s execution or even be an expression or value that you specify after the keyword ‘return’. And, after a return statement is executed, the program flow goes back to the state next to your function call and gets executed from there. So, to call a Python function at any place in your code, you will only have to use its name and pass arguments in its parentheses, if any. A return statement with no arguments is the same as a return None.

Example Demo of return statement.

Let’s Try

Lifetime  and Scope of Variables

A variable’s lifetime is the period of time for which it resides in the memory. The lifetime of variables inside a function is as long as the function executes. They are destroyed once you return from the function. Hence, a function does not remember the value of a variable from its previous calls.

All variables in a program may not be accessible at all locations in that program. This depends on where you have declared a variable. The variable declared in one part of the program may not be accessible to the other parts. The scope of a variable determines the portion of the program where you can access a particular identifier. There are two basic scopes of variables in Python.

  • Global scope
  • Local scope

Variables that are defined inside a function body have a local scope. In other words, it is local to that function and those defined outside have a global scope. This means that local variables can be accessed only inside the function in which they are declared, If you then try to access the variable x outside the function, it will give NameError.

Global variables can be accessed throughout the program body by all functions. When you call a function, the variables declared inside it are brought into scope, def func1 ( ) : x=9 # Local scope print(x) func1 ( ) >>> 9 >>> y=9 # Global scope def func2(): print(y) func2 ( ) >>> 9 >>> Let us discuss the global scope of a variable in brief. (a) The global names are variables assigned at the top level of the enclosing module file. It means that it is visible everywhere within the program. (b) The global names must be declared only if they are assigned within a function. (c) Global variables must be referenced within a function without being declared. See Example 14.

Example Demo of local and global variables.

When the above code is executed, it produces following result: Inside the function local total: 60 Outside the function global total: 0

global Statements

The global statements are remotely like declaration statements in Python. They do not type or size declarations, though they are namespace declarations. The global statement tells Python that a function plans to change one or more global names, i.e., names that live in the enclosing module’s scope (namespace).

A namespace is a practical approach to define the scope, and it helps to avoid name conflicts.

void FUNCTION

A function that doesn’t return a value is called a void function or non-fruitful function whereas a function that returns a value is called fruitful function, void functions might display something on the screen or have some other effect, but they don’t have a return value. If you try to assign the result to a variable, you get a special value called None.

A void function internally returns an empty value None.

Library  functions

Input ( ) is built-in functions, used to obtain informa-tion from the user.

>>> input ( ” Enter a number : ” ) Enter a number: 57 ‘ 57 ‘ Note that this returns the input as a string. If you want to take 57 as an integer, you need to apply the int() function to it. The int( ) converts a value to an integer.

>>> int(input(“Enter a number”)) Enter a number: 57 57

Example Demo of Input ( ) function.

The eval ( ) function converts a string containing a valid expression to an object. For example, x = 9 print(eval(‘x + 1’)) >>> 10 >>> >>> c = eval ( ” 3 , 5 , 6 ” ) # c = ( 3 , 5 , 6 ) >>> c ( 3 , 5 , 6 ) >>>

The print ( ) function enables a Python program to display textual information to the user, print(“Hello, World!”) print(“I am learning Python”)

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10 Python Practice Exercises for Beginners With Detailed Solutions

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A great way to improve quickly at programming with Python is to practice with a wide range of exercises and programming challenges. In this article, we give you 10 Python practice exercises to boost your skills.

Practice exercises are a great way to learn Python. Well-designed exercises expose you to new concepts, such as writing different types of loops, working with different data structures like lists, arrays, and tuples, and reading in different file types. Good exercises should be at a level that is approachable for beginners but also hard enough to challenge you, pushing your knowledge and skills to the next level.

If you’re new to Python and looking for a structured way to improve your programming, consider taking the Python Basics Practice course. It includes 17 interactive exercises designed to improve all aspects of your programming and get you into good programming habits early. Read about the course in the March 2023 episode of our series Python Course of the Month .

Take the course Python Practice: Word Games , and you gain experience working with string functions and text files through its 27 interactive exercises.  Its release announcement gives you more information and a feel for how it works.

Each course has enough material to keep you busy for about 10 hours. To give you a little taste of what these courses teach you, we have selected 10 Python practice exercises straight from these courses. We’ll give you the exercises and solutions with detailed explanations about how they work.

To get the most out of this article, have a go at solving the problems before reading the solutions. Some of these practice exercises have a few possible solutions, so also try to come up with an alternative solution after you’ve gone through each exercise.

Let’s get started!

Exercise 1: User Input and Conditional Statements

Write a program that asks the user for a number then prints the following sentence that number of times: ‘I am back to check on my skills!’ If the number is greater than 10, print this sentence instead: ‘Python conditions and loops are a piece of cake.’ Assume you can only pass positive integers.

Here, we start by using the built-in function input() , which accepts user input from the keyboard. The first argument is the prompt displayed on the screen; the input is converted into an integer with int() and saved as the variable number. If the variable number is greater than 10, the first message is printed once on the screen. If not, the second message is printed in a loop number times.

Exercise 2: Lowercase and Uppercase Characters

Below is a string, text . It contains a long string of characters. Your task is to iterate over the characters of the string, count uppercase letters and lowercase letters, and print the result:

We start this one by initializing the two counters for uppercase and lowercase characters. Then, we loop through every letter in text and check if it is lowercase. If so, we increment the lowercase counter by one. If not, we check if it is uppercase and if so, we increment the uppercase counter by one. Finally, we print the results in the required format.

Exercise 3: Building Triangles

Create a function named is_triangle_possible() that accepts three positive numbers. It should return True if it is possible to create a triangle from line segments of given lengths and False otherwise. With 3 numbers, it is sometimes, but not always, possible to create a triangle: You cannot create a triangle from a = 13, b = 2, and c = 3, but you can from a = 13, b = 9, and c = 10.

The key to solving this problem is to determine when three lines make a triangle regardless of the type of triangle. It may be helpful to start drawing triangles before you start coding anything.

Python Practice Exercises for Beginners

Notice that the sum of any two sides must be larger than the third side to form a triangle. That means we need a + b > c, c + b > a, and a + c > b. All three conditions must be met to form a triangle; hence we need the and condition in the solution. Once you have this insight, the solution is easy!

Exercise 4: Call a Function From Another Function

Create two functions: print_five_times() and speak() . The function print_five_times() should accept one parameter (called sentence) and print it five times. The function speak(sentence, repeat) should have two parameters: sentence (a string of letters), and repeat (a Boolean with a default value set to False ). If the repeat parameter is set to False , the function should just print a sentence once. If the repeat parameter is set to True, the function should call the print_five_times() function.

This is a good example of calling a function in another function. It is something you’ll do often in your programming career. It is also a nice demonstration of how to use a Boolean flag to control the flow of your program.

If the repeat parameter is True, the print_five_times() function is called, which prints the sentence parameter 5 times in a loop. Otherwise, the sentence parameter is just printed once. Note that in Python, writing if repeat is equivalent to if repeat == True .

Exercise 5: Looping and Conditional Statements

Write a function called find_greater_than() that takes two parameters: a list of numbers and an integer threshold. The function should create a new list containing all numbers in the input list greater than the given threshold. The order of numbers in the result list should be the same as in the input list. For example:

Here, we start by defining an empty list to store our results. Then, we loop through all elements in the input list and test if the element is greater than the threshold. If so, we append the element to the new list.

Notice that we do not explicitly need an else and pass to do nothing when integer is not greater than threshold . You may include this if you like.

Exercise 6: Nested Loops and Conditional Statements

Write a function called find_censored_words() that accepts a list of strings and a list of special characters as its arguments, and prints all censored words from it one by one in separate lines. A word is considered censored if it has at least one character from the special_chars list. Use the word_list variable to test your function. We've prepared the two lists for you:

This is another nice example of looping through a list and testing a condition. We start by looping through every word in word_list . Then, we loop through every character in the current word and check if the current character is in the special_chars list.

This time, however, we have a break statement. This exits the inner loop as soon as we detect one special character since it does not matter if we have one or several special characters in the word.

Exercise 7: Lists and Tuples

Create a function find_short_long_word(words_list) . The function should return a tuple of the shortest word in the list and the longest word in the list (in that order). If there are multiple words that qualify as the shortest word, return the first shortest word in the list. And if there are multiple words that qualify as the longest word, return the last longest word in the list. For example, for the following list:

the function should return

Assume the input list is non-empty.

The key to this problem is to start with a “guess” for the shortest and longest words. We do this by creating variables shortest_word and longest_word and setting both to be the first word in the input list.

We loop through the words in the input list and check if the current word is shorter than our initial “guess.” If so, we update the shortest_word variable. If not, we check to see if it is longer than or equal to our initial “guess” for the longest word, and if so, we update the longest_word variable. Having the >= condition ensures the longest word is the last longest word. Finally, we return the shortest and longest words in a tuple.

Exercise 8: Dictionaries

As you see, we've prepared the test_results variable for you. Your task is to iterate over the values of the dictionary and print all names of people who received less than 45 points.

Here, we have an example of how to iterate through a dictionary. Dictionaries are useful data structures that allow you to create a key (the names of the students) and attach a value to it (their test results). Dictionaries have the dictionary.items() method, which returns an object with each key:value pair in a tuple.

The solution shows how to loop through this object and assign a key and a value to two variables. Then, we test whether the value variable is greater than 45. If so, we print the key variable.

Exercise 9: More Dictionaries

Write a function called consonant_vowels_count(frequencies_dictionary, vowels) that takes a dictionary and a list of vowels as arguments. The keys of the dictionary are letters and the values are their frequencies. The function should print the total number of consonants and the total number of vowels in the following format:

For example, for input:

the output should be:

Working with dictionaries is an important skill. So, here’s another exercise that requires you to iterate through dictionary items.

We start by defining a list of vowels. Next, we need to define two counters, one for vowels and one for consonants, both set to zero. Then, we iterate through the input dictionary items and test whether the key is in the vowels list. If so, we increase the vowels counter by one, if not, we increase the consonants counter by one. Finally, we print out the results in the required format.

Exercise 10: String Encryption

Implement the Caesar cipher . This is a simple encryption technique that substitutes every letter in a word with another letter from some fixed number of positions down the alphabet.

For example, consider the string 'word' . If we shift every letter down one position in the alphabet, we have 'xpse' . Shifting by 2 positions gives the string 'yqtf' . Start by defining a string with every letter in the alphabet:

Name your function cipher(word, shift) , which accepts a string to encrypt, and an integer number of positions in the alphabet by which to shift every letter.

This exercise is taken from the Word Games course. We have our string containing all lowercase letters, from which we create a shifted alphabet using a clever little string-slicing technique. Next, we create an empty string to store our encrypted word. Then, we loop through every letter in the word and find its index, or position, in the alphabet. Using this index, we get the corresponding shifted letter from the shifted alphabet string. This letter is added to the end of the new_word string.

This is just one approach to solving this problem, and it only works for lowercase words. Try inputting a word with an uppercase letter; you’ll get a ValueError . When you take the Word Games course, you slowly work up to a better solution step-by-step. This better solution takes advantage of two built-in functions chr() and ord() to make it simpler and more robust. The course contains three similar games, with each game comprising several practice exercises to build up your knowledge.

Do You Want More Python Practice Exercises?

We have given you a taste of the Python practice exercises available in two of our courses, Python Basics Practice and Python Practice: Word Games . These courses are designed to develop skills important to a successful Python programmer, and the exercises above were taken directly from the courses. Sign up for our platform (it’s free!) to find more exercises like these.

We’ve discussed Different Ways to Practice Python in the past, and doing interactive exercises is just one way. Our other tips include reading books, watching videos, and taking on projects. For tips on good books for Python, check out “ The 5 Best Python Books for Beginners .” It’s important to get the basics down first and make sure your practice exercises are fun, as we discuss in “ What’s the Best Way to Practice Python? ” If you keep up with your practice exercises, you’ll become a Python master in no time!

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Learn about the different characteristics that make an algorithm effective.

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Etymology of the word algorithm

An algorithm is an explicit, precise, unambiguous, mechanically executable sequence of elementary instructions, usually intended to accomplish a specific purpose. For example, here is an algorithm for singing that annoying song “99 Bottles of Beer on the Wall” for arbitrary values of the starting number of beer bottles:

what is problem solving in python

Last week, we released Gemini 1.0 Ultra in Gemini Advanced. You can try it out now by signing up for a Gemini Advanced subscription . The 1.0 Ultra model, accessible via the Gemini API, has seen a lot of interest and continues to roll out to select developers and partners in Google AI Studio .

Today, we’re also excited to introduce our next-generation Gemini 1.5 model , which uses a new Mixture-of-Experts (MoE) approach to improve efficiency. It routes your request to a group of smaller "expert” neural networks so responses are faster and higher quality.

Developers can sign up for our Private Preview of Gemini 1.5 Pro , our mid-sized multimodal model optimized for scaling across a wide-range of tasks. The model features a new, experimental 1 million token context window, and will be available to try out in  Google AI Studio . Google AI Studio is the fastest way to build with Gemini models and enables developers to easily integrate the Gemini API in their applications. It’s available in 38 languages across 180+ countries and territories .

1,000,000 tokens: Unlocking new use cases for developers

Before today, the largest context window in the world for a publicly available large language model was 200,000 tokens. We’ve been able to significantly increase this — running up to 1 million tokens consistently, achieving the longest context window of any large-scale foundation model. Gemini 1.5 Pro will come with a 128,000 token context window by default, but today’s Private Preview will have access to the experimental 1 million token context window.

We’re excited about the new possibilities that larger context windows enable. You can directly upload large PDFs, code repositories, or even lengthy videos as prompts in Google AI Studio. Gemini 1.5 Pro will then reason across modalities and output text.

Upload multiple files and ask questions We’ve added the ability for developers to upload multiple files, like PDFs, and ask questions in Google AI Studio. The larger context window allows the model to take in more information — making the output more consistent, relevant and useful. With this 1 million token context window, we’ve been able to load in over 700,000 words of text in one go. Gemini 1.5 Pro can find and reason from particular quotes across the Apollo 11 PDF transcript. 
[Video sped up for demo purposes]
Query an entire code repository The large context window also enables a deep analysis of an entire codebase, helping Gemini models grasp complex relationships, patterns, and understanding of code. A developer could upload a new codebase directly from their computer or via Google Drive, and use the model to onboard quickly and gain an understanding of the code. Gemini 1.5 Pro can help developers boost productivity when learning a new codebase.  
Add a full length video Gemini 1.5 Pro can also reason across up to 1 hour of video. When you attach a video, Google AI Studio breaks it down into thousands of frames (without audio), and then you can perform highly sophisticated reasoning and problem-solving tasks since the Gemini models are multimodal. Gemini 1.5 Pro can perform reasoning and problem-solving tasks across video and other visual inputs.  

More ways for developers to build with Gemini models

In addition to bringing you the latest model innovations, we’re also making it easier for you to build with Gemini:

Easy tuning. Provide a set of examples, and you can customize Gemini for your specific needs in minutes from inside Google AI Studio. This feature rolls out in the next few days. 
New developer surfaces . Integrate the Gemini API to build new AI-powered features today with new Firebase Extensions , across your development workspace in Project IDX , or with our newly released Google AI Dart SDK . 
Lower pricing for Gemini 1.0 Pro . We’re also updating the 1.0 Pro model, which offers a good balance of cost and performance for many AI tasks. Today’s stable version is priced 50% less for text inputs and 25% less for outputs than previously announced. The upcoming pay-as-you-go plans for AI Studio are coming soon.

Since December, developers of all sizes have been building with Gemini models, and we’re excited to turn cutting edge research into early developer products in Google AI Studio . Expect some latency in this preview version due to the experimental nature of the large context window feature, but we’re excited to start a phased rollout as we continue to fine-tune the model and get your feedback. We hope you enjoy experimenting with it early on, like we have.

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COMMENTS

  1. Problem Solving in Python

    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.

  2. Solve Python

    Solve Challenge Write a function MediumPython (Basic)Max Score: 10Success Rate: 90.31% Solve Challenge Print Function EasyPython (Basic)Max Score: 20Success Rate: 97.25% Solve Challenge List Comprehensions EasyPython (Basic)Max Score: 10Success Rate: 97.71% Solve Challenge Find the Runner-Up Score!

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