Easy Python Programming for Absolute Beginners

Hello future Pythonistas! I'm Dr. Lawrence Gray, and I'm thrilled to welcome you to this exciting adventure into Python programming.

In this lecture, you will learn the basic principles of computing: what it really is, what a computer is made up of and how it works.

In this lecture, we will discuss a brief history of programming languages from low-level assembly language to high-level languages like C and C++. We will also talk about the uniqueness of Python and what makes it the most attractive programming language for beginners.

This course was specifically designed for absolute beginners. So whether you are an academic researcher, business professional, or just curious about Python this course is for you. This video covers the course structure and major concepts.

This quiz assesses your understanding of the main focus of the course, the identity of a specific Director of Engineering mentioned, and the recognition of a programming language related to statistical programming.What is this course about?

What is Google Colab?

Google Colab, short for Google Collaboratory, is an online platform that provides a cloud-based Python programming environment. It allows you to write and execute Python code through your web browser without having to install any software on your local machine. One of the significant advantages is the availability of free computing resources, including GPUs and TPUs, which makes it ideal for data analysis, machine learning, and other computational tasks.

Key Features:

• Zero Setup: No need to install anything; all you need is a Google account.

• Free Access to GPUs and TPUs: Accelerate your computations without incurring extra costs.

• Collaboration: Share notebooks with your peers or teachers and collaborate in real-time.

• Versatility: You can load data from various sources, use pre-installed libraries, and even install new ones.

• Integration with Google Drive: Store and manage your notebooks directly in Google Drive.

• Interactive Environment: Provides a mix of code and text cells to create interactive, well-documented notebooks.

Why We're Using Google Colab in This Course

Throughout this course, we'll be using Google Colab for several key reasons:

1. Accessibility: Google Colab is easily accessible from any device with internet access, making it easy for everyone to participate.

2. Consistency: It ensures that everyone is working in a similar environment, reducing the "it works on my machine" types of issues.

3. Ease of Use: Its user-friendly interface is great for beginners.

4. Resource Availability: The platform offers powerful computational resources for free, which is excellent for running complex algorithms and models.

5. Real-time Collaboration: You can work on projects together, making it easier to collaborate on assignments and study groups.

By using Google Colab, we can focus more on learning and less on troubleshooting software issues. You'll find it to be a very useful tool for coding exercises, assignments, and even your personal projects.

In this video, I will guide you through the process of registering for Google Colab. We will start by signing into Google and navigating to the Colab website. Then, we will open our Google Drive and install Google Collaboratory. Finally, we will open a Google Collaboratory Notebook directly from Google Drive. By the end of this video, you will be ready to use Google Colab for your projects.

To Follow along with the lectures, use Notebooks located under the Resources tab

The Jupyter Notebook and the Google Colab Notebook are the same. Sometimes, their names are used interchangeably

In this introductory lecture to Python programming, students will become familiar with the Jupyter Notebook tool also known as the Google Colab Notebook, learn how to write and execute code within cells, understand syntax highlighting, and recognize the importance of proper syntax. They'll execute their first line of code and learn debugging basics.

Students will be exposed to errors in Python and introduced to the Traceback. By analyzing the Traceback, they'll diagnose and fix issues, aiding in their growth as Python programmers.

This quiz tests your understanding of the primary coding platform used in the course, the functionality of a Jupyter Notebook and the identification of a particular coding error.

In this section, you will learn to create and name variables, perform mathematical calculations, apply the order of operations in Python, write effective comments, and build practical solutions like a BMI calculator

Variable assignment serves a crucial role in any programming language. It provides a means to store and retrieve data using a unique identifier or name. This is especially beneficial when dealing with complex data structures or when crafting your own functions. The ability to reference data using assigned variable names makes your code more readable, manageable, and efficient.

Variable assignment is achieved using an equal sign (=) operator. The item on the left of the operator is the name, or "namespace," assigned to represent the data, which is positioned on the right of the operator. Here are some fundamental rules regarding namespaces:

• Namespaces should begin with an alphabetical character, with no spaces or special characters allowed, except for underscores (_).

• From the point of assignment onwards, the right-side data can be referenced using the left-side name.

• These names or identifiers are collectively referred to as variables.

This video demonstrates how to write variable assignments with examples.

Best Practices for Variable Naming

Adopting a consistent and understandable variable naming convention enhances the readability and maintainability of your code, particularly for future users. Here are some widely-accepted conventions:

• Lowercase letters: Variable names should be entirely in lowercase.

• Use of underscores: Underscores should be used to represent spaces between words in a variable name.

• Descriptive names: Choose names that convey some sense about the type or purpose of the data it represents. It should be human-readable and meaningful to others who might interact with your code in the future.

By employing clear and meaningful variable names, you aid future developers who may read or maintain your code. This practice also helps you keep your own code organized, making it easier to navigate and troubleshoot.

This video is a short Introduction on the importance on math operators in Python

Description: In this lesson, students will embark on a comprehensive exploration of Python's math operators. The lesson is designed to introduce various arithmetic operations, including addition, subtraction, multiplication, division, integer division, modulo, and exponentiation. Practical examples and code snippets are provided to demonstrate how each operator functions.

Outcomes: Upon completing this lecture, students will be able to:

• Use Basic Math Operators: Understand and apply Python's fundamental math operators for addition (+), subtraction (-), multiplication (*), division (/), integer division (//), modulo (%), and exponentiation (**).

• Perform Arithmetic Calculations: Write Python code to perform various arithmetic operations and manipulate numerical data.

• Solve Practical Problems: Utilize the math operators to create solutions to real-world problems, enhancing their ability to develop efficient and effective code.

This lesson lays the groundwork for understanding mathematical computations in Python, an essential skill for any programmer, whether a beginner or an experienced developer.

Description: This lesson delves into the concept of in-place addition and subtraction in Python, using the += and -= operators. Through practical examples and explanations, students will understand how these operators work and where they can be applied.

Outcomes: After completing this lecture, students will be able to:

• Understand the syntax and functionality of in-place addition and subtraction.

• Apply these operators in their code to perform efficient calculations.

• Write cleaner and more concise code by using these shorthand methods.

The conclusion emphasizes the importance of understanding Python math operators for various arithmetic tasks.  Utilizing these operators enhances code efficiency and expressiveness.

This Lecture explains the significance of the order of operations in Python programming. Similar to mathematical rules, Python follows specific guidelines to determine the sequence of calculations in an expression with multiple operations. The principle "PEMDAS," representing Parentheses, Exponents, Multiplication and Division, and Addition and Subtraction, is explored. By understanding and following these rules, programmers can ensure that their code produces the intended results.

Description: In this demonstration, students are guided through the essential concept of the order of operations in Python programming. Using real-world examples, the content explores how Python follows specific rules, mirroring mathematical principles, to determine the sequence of calculations in an expression with multiple operations.

Outcomes: After completing this demonstration, students will be able to:

• Understand the Order of Operations: Recognize the importance and application of the order of operations in Python, following the "PEMDAS" principle.

• Apply Mathematical Principles: Use mathematical expressions to perform complex arithmetic operations, including multiplication, division, addition, subtraction, exponentiation, and more.

• Write Accurate Expressions: Construct Python expressions that align with the intended mathematical calculations, ensuring the correct results.

• Solve Real-World Problems: Utilize the understanding of the order of operations to create accurate solutions for real-world problems, enhancing coding efficiency and precision.

This Lecture provides a short introduction on the concept of "comments" in python programming

Description: Provides a demonstration about using different types of comments

Outcome:  After this demonstration, you should be able to annotate your code

These concluding remarks about comments describe why you should use comments and six best practices for writing them.

Demonstrate your understanding of above topics.

Details:

1. Functions: The first half of the section demystifies the concept of functions in Python. Students will learn about common functions like print(), len(), and input(), and understand how to use these reusable code blocks to make code interactive and dynamic.

2. Modules: The second half introduces the magic of the import statement. Students will discover how to tap into Python's vast ecosystem, using modules like the math module for mathematical functions and the random module for generating random numbers.

In this enlightening session, students are introduced to the wonderful world of Python's built-in functions. Focusing on three fundamental functions—print(), len(), and input()—this lecture aims to equip aspiring Python programmers with essential tools to enhance coding efficiency.

This demonstration provides a comprehensive exploration of Python's built-in functions, focusing on the essential tools that enable code reuse and efficiency. It covers the introduction to functions, usage of the print() function, understanding of the len() function, diving into the input() function, and dissecting the structure of a function call.

This video introduction invites learners to explore the remarkable capabilities of Python's import statements. It emphasizes the importance of understanding how to integrate pre-existing code and access Python's extensive libraries, thereby enhancing code functionality and efficiency.

This video demonstration provides an in-depth look into Python's import statement, including importing modules, specific items from modules, and using aliases. It also delves into the practical application of the random module for generating random numbers.

After completing this demonstration, students will be able to:

• Import Modules Seamlessly: Utilize the import statement to integrate various Python modules into their code, enriching functionality.

• Customize Imports: Import specific items or use aliases to tailor the import process to their needs, improving code readability and efficiency.

• Generate Random Numbers: Apply the random module to create random numbers for a wide range of applications.

This quiz is carefully crafted to test students' knowledge and understanding of import statements and built-in functions in Python, specifically focusing on the random module and common functions like print() and input().

This enriching section equips students with the skills needed to understand Python's diverse data types, handle files, and even understand the development a simple game. Starting with foundational data types like integers and strings, you'll move to lists, tuples, dictionaries, and sets. The lecture also covers literals, type-casting, and file management, providing a holistic Python learning experience. As a capstone, you'll dive into a game demonstration, seeing how the skills you have acquired work in a practical setting with the "QuickGuess: The 2-Point Challenge" game.

What You'll Learn:

• Core and Complex Data Types: Master Python's basic and collection data types like integers, strings, lists, tuples, dictionaries, and sets.

• Literals and Type-Casting: Understand how to use literals and convert between data types.

• String and File Operations: Learn various string manipulations and file management techniques.

• Game Development Basics: Get introduced to game development in Python through the "QuickGuess: The 2-Point Challenge" game demonstration.

Skills You'll Gain:

• Data Representation and Manipulation: Be proficient in representing and manipulating data using various Python data types.

• Type Conversion: Understand the principles of type-casting for seamless data conversion.

• File Management: Acquire the skills for effective file operations.

• Game Logic and Flow: Gain insights into game development, understanding game logic, scoring, and winning conditions.

By the End of This Lecture, You Will Be Able To:

• Select and manipulate the right data types for different programming needs.

• Perform type-casting between different data types effectively.

• Handle files for a range of tasks, from basic read-write operations to more complex manipulations.

• Develop a simple Python game, understanding its logic, scoring mechanisms, and how to declare a winner.

Special Focus: QuickGuess: The 2-Point Challenge

• What is it?: A Python-based number guessing game involving multiple players.

• Skills Applied: Data representation using dictionaries for scoring, use of sets for guesses, and basic control flow for game logic.

• Outcome: Understand how to apply Python data types and control structures in a real-world game scenario.

Description: This lecture introduces literals in Python, with a focus on strings, integers, floating-point numbers.

Outcomes:

• Students will be able to declare and manipulate various types of literals including strings, integers, and floats.

Description: This section introduces literals in Python, with a focus on booleans. It also discusses the concept of truthiness and falseness and provides an introduction to type casting.

Outcomes:

• Students will be able to declare booleans.

• Students will be able to identify and apply the concepts of truthiness and falseness in Python code.

• Students will be able to perform type casting between different data types.

Description:

This lecture introduces tuples in Python, with a focus on their anatomy, how they differ from lists, and why they are a memory-efficient choice.

Outcomes:

• Students will be able to create and access elements within tuples.

• Students will be able to distinguish between tuples and lists and decide when to use each.

• Students will understand the memory efficiency of using tuples over lists in appropriate scenarios.

Description: This lecture provides an introduction to lists and sets in Python. It explains how to access list items by index and introduces the anatomy of a set.

Outcomes:

• Students will be able to create, modify, and access elements within lists.

• Students will be able to use indexing to retrieve specific items from a list.

• Students will be able to create and manipulate sets, as well as understand their unique characteristics.

Description:

This lecture provides a comprehensive overview of dictionaries in Python, focusing on storing key-value pairs and the anatomy of a dictionary.

Outcomes:

• Students will be able to create dictionaries.

• Students will be able to access values from dictionaries using keys.

Description:

This lecture introduces the NoneType and file-like objects in Python. It covers the basics of file handling and the anatomy of a file-like object.

Outcomes:

• Students will understand the concept of NoneType and know when and where to use None in their code.

• Students will be able to perform basic file operations like reading from and writing to files.

• Students will understand what file-like objects are and how to work with them.

Description:

This demonstration covers the game "QuickGuess: The 2-Point Challenge". Topics include an overview of the game, its components such as winning conditions, game flow, output, and conclusion.

Skills:

After completing this lecture, students will be able to understand and apply the concepts of game design and flow covered in this notebook. They will get insights into different components like players, scores, and game logic.

This quiz aims to assess your understanding of fundamental data types in Python. The questions cover various topics, such as the purpose of data types, the representation of textual and collection data, and the mutability of these types. By the end of this quiz, you should be able to demonstrate your grasp of Python data types and their characteristics.

• Explore String Interpolation: Learn the fundamentals of string interpolation and its importance in Python for constructing dynamic strings.

• Understand Various Methods: Get introduced to different string formatting methods including the old-style % operator and the str.format() method.

• Master F-Strings: Focus on F-strings, a feature in Python 3.6, to create readable and efficient string formatting.

This lecture is an in-depth guide to mastering string interpolation in Python, an essential skill for constructing dynamic strings. Students will explore different methods of string formatting, with a specific focus on F-strings, a feature introduced in Python 3.6. The lecture also distinguishes between string formatting methods and the print() function, which outputs text to the console.

What You'll Learn:

• Introduction to String Interpolation: Understand what string interpolation is and why it's crucial in Python programming.

• Old-style % Operator: Briefly touch upon the old-style % operator used for string formatting.

• The str.format() Method: Learn about another common method for string formatting in Python.

• F-Strings: Dive deeply into F-strings, exploring their syntax and use-cases.

Skills You'll Gain:

• Dynamic String Construction: Learn to construct dynamic strings using various string interpolation methods.

• Text Output: Understand how to use the print() function for console output in conjunction with string formatting.

• Code Readability: Improve code readability and maintainability by using concise string formatting methods like F-strings.

By the End of This Lecture, You Will Be Able To:

• Use different string interpolation methods to dynamically construct strings.

• Understand when and how to use the print() function for text output.

• Implement F-strings effectively in your Python code to improve readability and efficiency.

Welcome to this quiz on Python F-strings! This quiz is designed to test your understanding of F-strings in Python, a feature that simplifies string formatting and makes your code more readable.

Methods are essential tools that allow specific actions on data types. Throughout this section we will explore how methods differ from functions, understand their structure, and practice using them with both strings and data structures such as lists and dictionaries. By the end, you'll possess a solid foundation and employing methods to streamline your coding tasks and enhance the functionality of your programs.

This lecture aims to clarify the concept of methods in Python, delineating the differences between methods and functions, their structure, and their applications. The lecture walks students through various examples, explaining how methods are inherently tied to objects and how they differ from generic functions.

Content Highlights:

1. Introducing Methods: Understand the fundamental distinction between functions and methods.

2. A Method's Contrast to Functions: Learn why methods are specific to objects and how they differ from functions.

3. The Structure of a Method: Decode the anatomy of a method and how to call one.

4. Distinction Between Methods and Functions: Uncover how methods can take multiple arguments and modify the state of an object.

What Students Will Learn:

• Differentiate between methods and functions in Python.

• Understand the structure of a method and how to call it.

• Apply methods that are specific to different data types like strings.

• Utilize built-in Python functions like dir and help to explore methods.

By the end of this lecture, students will be equipped with the knowledge to effectively use and understand methods in Python, paving the way for more advanced programming tasks.

1. Method Specificity to Data Types: Explore how methods can be specific to certain data types like lists and dictionaries.  As well as apply methods that are specific to different data types.

In this class demonstration, "Capital Conundrum," we'll dive into Python dictionaries and random selection to build a quiz game about countries and their capitals. The code defines a dictionary that pairs countries with their capitals. Then it randomly selects a country and asks the user to guess its capital. The program loops until the user correctly identifies the capital, comparing the guess to the correct answer in a case-insensitive manner. By studying this demonstration, you'll reinforce your understanding of Python dictionaries, user input, loops, and string comparison.

In "The Magic Bag" demonstration,  we dive into Python's powerful list methods through a fun and interactive game. Imagine you have a magical bag: you can add items to it, take things out, and peek inside whenever you wish. This demonstration uses simple code to simulate these actions, introducing students to critical list methods like .append() and .pop(). Players interact by choosing from a menu of options: adding an item, removing the last item, viewing the bag's contents, or exiting the game. By participating in this engaging hands-on activity, students will gain practical experience in list manipulation, a foundational concept for data handling in Python.

In this section, we will delve deep into each comparison operator, understand their intricacies, and explore the concept of object identity in Python. By the end of this section, you will be well-equipped to make informed decisions in your code using comparisons.

• Description: This section serves as an introduction to the topic of comparisons in Python. It provides a broad overview of comparison operators like ==, !=, <, >, <=, and >=, explaining their significance in decision-making and control flow within a Python program.

• Learning Outcome: After completing this section, students will be able to understand what comparison operators are and how they are used in Python to evaluate and compare values. They will also be familiar with the basic syntax for employing these operators in their code.

• Description: This part of the notebook delves into specific examples that illustrate the usage of comparison operators. It shows how these operators work in real-world examples involving numerical values, strings, and even lists.

• Learning Outcome: Upon completion of this section, students will be able to apply various comparison operators in different contexts. They will gain hands-on experience and confidence in employing these operators in their own Python projects.

• Description: This section explores the concept of object identity in Python, focusing on the difference between == and is. It emphasizes the importance of understanding object identity when working with mutable and immutable types.

• Learning Outcome: After finishing this section, students will be able to distinguish between equality and identity in Python. They will understand when to use == and is appropriately, particularly in the context of mutable and immutable objects.

This section is about control flow in Python. We will learn how to execute different code paths based on specific conditions, iterate over sequences, and manipulate the course of your code using various Python constructs.

Outcomes:

• Understand the fundamentals of control flow in Python.

• Learn to write conditional statements using if, elif, and else.

• Use while and for loops for iteration.

• Utilize Python's range and enumerate functions for more advanced looping.

• Know when to use pass, break, and continue to control your loops.

• Understand how logical and/or operators can be used in control flow.

Description

In this video, you'll get an introduction to control flow in Python, focusing on if/elif/else statements. We'll go through the basic structure and composition of an if statement and provide practical examples.

Learning Outcomes

• Understand what control flow is.

• Learn the basic structure of an if statement.

• Be able to write simple if statement.

Description

Continuing from the previous video, we delve into more advanced aspects of if/elif/else statements, including the importance of proper spacing and the use of elif.

Learning Outcomes

• Understand the importance of indentation in Python.

• Learn how to use elif in conditional statements.

• Be able to construct more complex if/elif statements.

Description

We'll explore the else statement and its applications in control flow. Additionally, you'll learn how to nest conditional structures for more complex logic.

Learning Outcomes

• Understand the functionality and use-cases for the else statement.

• Learn to write normal and nested if/elif/else statements.

Description

This video introduces while loops in Python, detailing their essential principles and operations. We'll also provide illustrative examples and discuss the structure of a while loop.

Learning Outcomes

• Understand the basic structure of a while loop.

• Learn how to write and use while loops in Python.

Description:

The Jupyter Notebook provides an introductory explanation of game loops, focusing on their essential role in video game development. It outlines the key components and functions that are integral to any game loop, especially in Python-based game projects.

What a Student Would Learn:

Upon completing the notebook, a student would gain foundational knowledge of what a game loop is, its significance in video game programming, and the basic elements that constitute it. This understanding would prepare the student for creating or analyzing game loops in future projects.

Description

We'll cover for loops, a crucial element of Python programming.  Learn how to nest for loops within each other for more complex operations, including working with multi-dimensional data structures.

Learning Outcomes

• Understand what an iterable is.

• Learn the basic structure and mechanics of for loops.

• Understand the concept of nested loops.

• Learn to write nested for loops for various scenarios.

Description

This video dives into the more advanced features of for loops, including the range function and its applications.  We'll cover the enumerate() function, a handy tool for looping through iterables, and the use of the underscore variable _ in Python.

Learning Outcomes

• Learn how to use the range function in for loops.

• Understand the concept of step size in range.

• Learn how to use enumerate() in loops.

• Understand the use-cases for the underscore variable _.

Description

We'll look at control statements like pass, break, and continue and how they are used to control the flow of loops.

Learning Outcomes

• Understand the use-cases for pass, break, and continue.

• Learn to implement these control statements in for and while loops.

Description

This video will cover logical operators and & or and their applications in Python programming. We'll also look at how to craft complex logical expressions.

Learning Outcomes

• Understand the and & or logical operators.

• Learn to write complex logical expressions combining and & or.

Description:

This lecture is about Python comprehensions. It covers list comprehensions. Through well-structured explanations, code samples, and practical examples, the lecture empowers you to write more efficient and cleaner Python code.

Outcomes:

By the end of this notebook, students will be able to:

1. Understand Comprehensions: Gain a solid understanding of what comprehensions are in Python and when to use them.

2. Master List Comprehensions: Be proficient in creating lists using comprehensions, effectively bypassing the need for for loops.

3. Utilize Conditionals: Integrate if conditions within list and dictionary comprehensions for more advanced data structures.

4. Write Efficient Code: Apply your knowledge to write cleaner, faster Python code that adheres to best practices.

5. Enhance Code Readability: Know when and how to use comprehensions to make your code more readable.

Description:

This lecture is about Python comprehensions. It covers dictionary comprehensions. Through well-structured explanations, code samples, and practical examples, the lecture empowers you to write more efficient and cleaner Python code.

Outcomes:

By the end of this notebook, students will be able to:

1. Understand Comprehensions: Gain a solid understanding of what comprehensions are in Python and when to use them.

2. Excel in Dictionary Comprehensions: Understand how to create dictionaries dynamically using dictionary comprehensions.

3. Utilize Conditionals: Integrate if conditions within list and dictionary comprehensions for more advanced data structures.

4. Write Efficient Code: Apply your knowledge to write cleaner, faster Python code that adheres to best practices.

5. Enhance Code Readability: Know when and how to use comprehensions to make your code more readable.

This lectures guides students through the creation of a card-based monster battle game. Utilizing dictionaries and lists, students will simulate card battles between player-controlled and computer-controlled monsters. Upon completion, students will:

• Understand how to manipulate lists and dictionaries in Python

• Implement game loops and conditional logic to simulate battles

• Make random selections to simulate card draws and monster actions