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AmjadAziz98/Adv_python

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Adv_python

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Overview

Adv_python is a repository focused on advanced Python concepts, techniques, and practical examples, with a primary emphasis on Jupyter Notebooks. This project is designed for learners and professionals who want to deepen their Python knowledge and explore advanced topics interactively.

Features

  • πŸ“’ Jupyter Notebooks: Interactive notebooks to demonstrate advanced Python concepts.
  • 🐍 Python Scripts: Supplementary Python (.py) files for select topics.
  • πŸš€ Hands-On Examples: Real-world use cases and code samples.
  • πŸ“š Well-Documented: Clear explanations and comments for easy understanding.

Topics Covered

While the exact contents may evolve, you can expect to find topics such as:

  • Advanced data structures and algorithms in Python
  • Decorators, generators, and context managers
  • Object-oriented programming (OOP) best practices
  • Functional programming techniques
  • Error handling and custom exceptions
  • Working with external libraries and APIs
  • Performance optimization and profiling
  • And more!

Getting Started

Prerequisites

Installation

Clone the repository:

git clone https://github.com/AmjadAziz98/Adv_python.git
cd Adv_python

Install dependencies (if any):

pip install -r requirements.txt

Usage

Start Jupyter Notebook in the project directory:

jupyter notebook

Open any notebook (.ipynb) file and start exploring!

Contributing

Contributions, suggestions, and improvements are welcome! Please open an issue or submit a pull request.

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

License

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

Acknowledgments

  • Inspired by the Python community and open-source educators.
  • Thanks to all contributors and users.

Note: This repository is composed of approximately 99.6% Jupyter Notebook and 0.4% Python files.

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