This repository hosts various Jupyter notebooks dedicated to exploring and implementing Python concepts, architectures, and libraries. The goal is to provide practical, hands-on examples and explanations of key Python techniques and libraries, helping users understand and apply these concepts in their projects.
- abc_vs_protocols_vs_mixins.ipynb: A notebook exploring the differences and use cases of Abstract Base Classes (ABCs), Protocols, and Mixins in Python.
- closures.ipynb: A notebook explaining closures in Python, how they work, and their practical applications.
- collections.ipynb: A notebook covering the collections module in Python, highlighting its specialized container datatypes.
- decorators.ipynb: A notebook demonstrating the use of decorators in Python, including how to create and apply them.
- functools.ipynb: A notebook detailing the functools module, which provides higher-order functions that operate on or return other functions.
- generators.ipynb: A notebook explaining generators in Python, including how to create them and their benefits for memory-efficient iteration.
- itertools.ipynb: A notebook exploring the itertools module, which provides functions for creating iterators for efficient looping.
- memory_management_and_concurrency.ipynb: A notebook discussing memory management and concurrency mechanisms in Python.
- metaclasses.ipynb: A notebook delving into metaclasses in Python and their role in customizing class creation.
- miscellaneous.ipynb: A notebook containing a variety of Python topics and tips that don't fit into other categories.
- regular_expressions.ipynb: A notebook providing an overview of regular expressions in Python for pattern matching and text processing.
- unit_tests.ipynb: A notebook on unit testing in Python, demonstrating how to write and run tests to ensure code quality and correctness.
- cohesion-coupling.ipynb: A notebook discussing the concepts of cohesion and coupling in software design.
- ddd.ipynb: An introduction to Domain-Driven Design (DDD) and its principles.
- dependency_injection.ipynb: A guide on implementing dependency injection in Python.
- hexagonal.ipynb: A notebook exploring the Hexagonal Architecture (also known as Ports and Adapters).
- SOLID.ipynb: A detailed explanation of the SOLID principles in object-oriented design.
- numpy.ipynb: An introduction to NumPy, a fundamental package for scientific computing in Python.
- pandas.ipynb: A notebook on Pandas, a powerful data manipulation and analysis library.
- pydantic.ipynb: A guide on using Pydantic for data validation and settings management using Python type annotations.
- scikit-learn.ipynb: A comprehensive introduction to scikit-learn, a machine learning library for Python.
- Here you can see several design pattern explanations and implementations.
To use this repository:
- Clone the repository to your local machine.
- Ensure you have Jupyter Notebook or JupyterLab installed, or use Google Colab to open the notebooks.
- Navigate to the repository directory and launch the notebooks using Jupyter Notebook or JupyterLab.
- Follow the instructions within the notebooks to explore various Python techniques and their implementations.
The notebooks in this repository cover a range of tools and techniques essential for Python programming, including:
- Software Design Principles: Cohesion, Coupling, SOLID, Domain-Driven Design, Hexagonal Architecture.
This repository and its contents aim to provide a practical understanding of key Python concepts and libraries, helping users develop efficient, maintainable, and scalable Python applications. Through detailed explanations and hands-on demonstrations, users can gain insights into various aspects of Python programming.
Contributions to this repository are welcome. Please read the CONTRIBUTING.md file for guidelines on how to contribute.
This project is licensed under the MIT License - see the LICENSE file for details.