This collection of Jupyter notebooks provides my notes on Python refreshment of topics.

This tutorial series is organized into four progressive modules:

Get started with Python fundamentals including:

• Print statements and "Hello World"
• Python version checking
• Data types and type conversions
• Mathematical and assignment operators
• Comparison operators
• String manipulation and formatting

Master Python's core data structures:

• Lists: Creating, accessing, modifying, and manipulating lists
• Tuples: Immutable sequences and nested tuples
• Dictionaries: Key-value pairs and nested dictionaries
• Sets: Unique collections and set operations (union, intersection, difference)

Build programming logic with:

• Conditional operators and comparison statements
• Control flow (if, elif, else)
• Logical operations (and, or, not)
• Loops (for, while, nested loops)
• Functions with parameters and default values
• Exception handling (try-except-finally)

Dive into object-oriented programming:

• Classes and Objects: Creating and using classes
• Constructors: Initializing objects
• Inheritance: Single, multiple, multilevel, hybrid, and hierarchical
• Polymorphism: Method overloading, overriding, duck typing, operator overloading
• Encapsulation: Private variables and public methods
• Data Abstraction: Abstract base classes and methods

Master numerical computing with NumPy:

• Basic Arrays: Creating and accessing arrays
• Array Attributes: Understanding shape, dtype, ndim, and size
• Array Creation: zeros, ones, arange, linspace, random arrays
• Sorting and Arrangement: Sorting arrays and concatenation
• Array Reshaping: Changing array dimensions
• Array Arithmetic: Element-wise operations and dot products
• Mathematical Functions: Trigonometric and statistical functions

Work with data structures and analysis using Pandas:

• DataFrames: Creating and manipulating data frames
• Data Selection: Accessing rows and columns
• Data Exploration: head(), tail(), describe() methods
• Finding Elements: Filtering and locating specific data
• Data I/O: Saving and loading data in CSV format
• Multiple Formats: Working with Excel, JSON, HTML, and SQL data sources

Master asynchronous programming with asyncio:

• What is Asyncio: Understanding asynchronous I/O and concurrent code
• Basic asyncio example: Writing simple async/await functions
• Fetching Data with async/await: Non-blocking operations and data retrieval
• Event Loop Timing: Understanding event loop execution and timing

Build simple web APIs with Flask:

• Basic Flask endpoint: Creating routes and returning JSON responses
• Query parameters: Reading values from the request URL
• GET and POST examples: Handling query strings and JSON payloads
• Runnable examples: Launchable snippets demonstrating endpoints

• Python 3.x installed on your system
• Jupyter Notebook or VS Code with Python extension

1. Clone or download this repository
2. Open the notebooks in sequential order (01 → 08)
3. Run the code cells to see outputs and experiment with modifications
4. Each notebook contains practical examples with detailed comments

Each notebook follows a hands-on approach:

• Clear explanations with inline comments
• Practical examples demonstrating concepts
• Executable code you can run and modify
• Progressive complexity building on previous lessons

• Beginners starting their Python journey
• Developers refreshing their Python knowledge
• Students learning programming fundamentals
• Anyone preparing for Python interviews or projects

• All code examples are tested and include output demonstrations
• Feel free to modify and experiment with the code
• Each notebook has a Table of Contents for easy navigation

Feel free to contribute by:

• Reporting issues or bugs
• Suggesting improvements or additional topics
• Adding more examples or exercises

Happy Learning! 🐍✨