Welcome to Learning Modern Python — a hands-on, notebook-driven journey through Python's core concepts and modern best practices. This repository is designed to guide learners from foundational topics to advanced features, emphasizing clean code, type safety, and real-world applications.
- Progressive Learning: Structured content from basic to advanced topics.
- Interactive Notebooks: Engage with code examples and exercises in real-time.
- Modern Practices: Emphasis on type hinting and data validation using
nptypingandpandera. - Real-World Applications: Practical examples to illustrate concepts.
Learning-modren-Python/
├── .vscode/ # VSCode configuration files
├── OOP_Pillars_thoery/ # Theoretical insights into OOP principles
├── day2.py # Introduction to Python syntax
├── day3_list_methods.ipynb # Working with lists and their methods
├── day4_day5__list_tuple_for_loops.ipynb # Lists, tuples, and loop constructs
├── day6_conditins.ipynb # Conditional statements
├── day7_Dictionary.ipynb # Dictionaries in Python
├── day8_Dict_methods.ipynb # Dictionary methods
├── day9_loops.ipynb # Looping mechanisms
├── day10_Functions.ipynb # Defining and using functions
├── day11_Try_except.ipynb # Exception handling
├── day12_Err_or_File_handling.ipynb# Error and file handling
├── day13_OOP.ipynb # Object-Oriented Programming basics
├── day14_OOP_Advance.ipynb # Advanced OOP concepts
├── day15_Encp_Abstract_callable.ipynb # Encapsulation, abstraction, and callables
├── day16_Numpy_nptyping.ipynb # NumPy with type annotations
├── day17_pandas.ipynb # Introduction to pandas
├── day18_DataFrame_pandasColumnFun.ipynb # DataFrame operations and column functions
├── day19_pandas_AI_enss.ipynb # Pandas in AI applications
├── abc.txt # Miscellaneous notes
├── requirements.txt # Project dependencies
└── README.md # Project overview and documentation
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Clone the Repository:
git clone https://github.com/MuazamMughal/Learning-modren-Python.git cd Learning-modren-Python -
Install Dependencies:
Ensure you have Python 3.x installed. Then, install the required packages:
pip install -r requirements.txt
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Launch Jupyter Notebook:
Start the Jupyter Notebook server:
jupyter notebook
Navigate to the desired notebook to begin learning.
- Python Basics: Syntax, data types, and control structures.
- Data Structures: Lists, tuples, dictionaries, and sets.
- Functions: Definition, scope, and higher-order functions.
- Object-Oriented Programming: Classes, inheritance, encapsulation, and polymorphism.
- Error Handling: Try-except blocks and file operations.
- Advanced Topics: Type hinting with
nptyping, data validation withpandera, and using pandas for data analysis.
Contributions are welcome! If you have suggestions, improvements, or additional topics to add, please fork the repository and submit a pull request.
This project is open-source and available under the MIT License.
Special thanks to the Python community and the developers of nptyping and pandera for their invaluable tools that enhance code reliability and readability.
Happy Coding! 🚀