🐍 Python Learning Repository

📌 Overview

This repository contains my comprehensive notes and practice code in Python.
It covers everything from fundamentals to advanced Python concepts, including data structures, OOP, best practices, modules, libraries, and debugging techniques.

I created these notes by studying structured courses from trusted platforms and practicing hands-on with Jupyter Notebook, Jupyter Lab, and Spyder IDE.

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📖 Table of Contents

• Why This Repo?
• Topics Covered
• Learning Resources
• Tools & IDEs Used
• Contributing
• License
• Author

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💡 Why This Repo?

• To build a strong foundation in Python programming.
• To serve as a reference guide for revision and projects.
• To integrate best practices in coding and problem-solving.
• To help others who want to master Python step by step.

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✅ Topics Covered

• Python Fundamentals
• Data Structures in Python
• Object-Oriented Programming (OOP) Concepts
• List Comprehensions & Python Best Practices
• Modules:
  • datetime
  • csv
  • os
  • subprocess
  • requests
  • inspect
  • xml
  • configparser
• Testing & Debugging
• Lambda Expressions & Decorators
• Profiling & Performance Optimization
• Threading & Concurrency
• Libraries:
  • NumPy
  • Pandas
  • Web Scraping (BeautifulSoup, Requests, etc.)

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📚 Learning Resources

These concepts were learned and practiced from the following courses:

1. 🎓 Diploma in Cloud Cyber Security – Python Module (AlNafi.com)
2. 🎓 Python for Data Science and AI (IBM Data Analysis Specialization – Coursera)

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🖥️ Tools & IDEs Used

• Jupyter Notebook
• Jupyter Lab
• Spyder IDE

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🤝 Contributing

Contributions are welcome! If you’d like to improve these notes:

1. Fork this repository
2. Create a new branch (git checkout -b feature-branch)
3. Commit your changes (git commit -m "Added new concepts or improvements")
4. Push to the branch (git push origin feature-branch)
5. Open a Pull Request 🚀

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📜 License

This project is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).

✔ You are free to:

• Share — copy and redistribute the material in any medium or format
• Adapt — remix, transform, and build upon the material

⚠ Under the following terms:

• Attribution — You must give appropriate credit.
• Non-Commercial — You may not use the material for commercial purposes.

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🙌 Author

KASHIF MAQBOOL JOIYA
🎓 Data Analyst & Data Scientist | Aspiring AI Engineer
💻 Passionate about Open Source, Data Science, Big Data, and AI Systems

🌐 Connect with me:

• GitHub
• LinkedIn