⚡python-learning-path

This repository contains a comprehensive collection of Python programming materials, from fundamental concepts to advanced topics. It's structured as a complete learning path for Python developers at all skill levels.

Repository Structure

Main Learning Paths

• language_basics/ - Python syntax, input/output, operators, flow control
• data_types/ - Strings, lists, tuples, sets, dictionaries and practice examples
• functions/ - Function definitions, parameters, return values, scope
• modules/ - Creating and importing modules
• packages/ - Working with Python packages
• oop_basics/ - Classes, objects, constructors, methods, variables
• inheritance/ - Types of inheritance, method resolution
• advanced_oop/ - Inner classes, polymorphism, operator overloading, abstract classes
• exception_handling/ - Try-except blocks, custom exceptions, finally clauses
• file_handling/ - Reading/writing files, binary data, directory operations
• multithreading/ - Thread creation, synchronization, communication
• decorators/ - Function decoration, practical examples
• generators/ - Generator functions and expressions
• regex/ - Regular expressions patterns and applications
• logging/ - Python's logging module implementation
• serialization/ - Object serialization, JSON, YAML
• database/ - Database connectivity and operations
• mini_projects/ - Practical applications of learned concepts

Practice Materials

• practice/patterns/ - Programming pattern exercises
• practice/interview_questions/ - Common Python interview questions

Support Files

• course_materials/pdfs/ - Educational PDFs and reference documents
• data_files/ - Sample data files for exercises
  • Text files, CSV, JSON, images, ZIP archives, log files

Notebook Contents

Language Fundamentals

• Python syntax basics
• Input/output operations
• Operators and expressions
• Flow control (conditions, loops)

Data Types

• String manipulation
• List operations and examples
• Tuple data structure
• Set operations
• Dictionary usage and examples
• Deep dive into each data structure

Functions & Modules

• Function definition and usage
• Module creation and importing
• Package structure and management

Object-Oriented Programming

• Class introduction and implementation
• Self variable and constructors
• Instance and static methods
• Variable scopes
• Inner/nested classes
• Garbage collection
• Inheritance hierarchies
• Method resolution order
• Polymorphism
• Operator overloading
• Abstract classes and interfaces
• Access modifiers (public, private, protected)
• Special methods (__str__, __repr__)

Exception Handling

• Try-except blocks
• Exception hierarchy
• Custom exceptions
• Finally blocks
• Nested exception handling

File Operations

• File reading/writing
• Context managers (with statement)
• Binary file operations
• Directory management

Multithreading

• Thread creation and management
• Daemon threads
• Thread synchronization
• Locks and semaphores
• Thread communication

Advanced Topics

• Decorators implementation
• Generator functions and expressions
• Regular expressions patterns
• Logging implementation
• Object serialization techniques
• Database connectivity
• Web scraping basics

Getting Started

Prerequisites

• Python 3.6 or higher
• Jupyter Notebook or JupyterLab for running the notebooks

Installation

1. Clone this repository:

https://github.com/Akshayredekar07/python-learning-path.git
cd python-learning-path

2. Create a virtual environment (optional but recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install required packages:

pip install -r requirements.txt

4. Launch Jupyter Notebook:

jupyter notebook

How to Use This Repository

For Beginners

1. Start with the language_basics folder to understand Python fundamentals
2. Progress through data_types to learn about built-in data structures
3. Continue with functions, modules, and packages
4. Move to oop_basics for an introduction to object-oriented programming

For Intermediate Learners

1. Explore inheritance and advanced_oop to deepen OOP knowledge
2. Study exception_handling and file_handling for I/O operations
3. Dive into multithreading for concurrent programming concepts
4. Work through decorators, generators, and regex modules

For Advanced Users

1. Focus on database for data persistence techniques
2. Explore serialization for object serialization strategies
3. Study logging for application monitoring
4. Complete mini_projects to apply knowledge to real problems
5. Practice with interview_questions for skill reinforcement

Example Code

Each topic folder contains example code and exercises to practice the concepts covered. The examples subdirectory in each section contains additional implementations.

Reference Materials

The course_materials/pdfs/ directory contains supplementary learning resources including:

• Python programming guide
• NumPy reference
• Additional learning materials

Contributing

Contributions to improve the course materials are welcome. Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.