Python Zero to Mastery
About:
This repository is an evolving resource aimed at exploring the Python programming language in depth. It focuses on hot topics and practical libraries to provide a comprehensive understanding of Python. The repository is regularly updated to include the latest developments in Python programming.
Structure
1. Logging
Files: logging_zero_to_mastery.ipynb, logging_zero_to_mastery.py
Covers the basics and advanced concepts of logging in Python.
- Basic Logging Examples
- Logging in Larger Applications
- Logging to a File
- Using a Logger Object
- Rotating Log Files
- Logging Exceptions
- Custom Level Logging
Outcome: Enables efficient debugging and monitoring using Python’s logging capabilities.
2. Testing
File: testing_zero_to_mastery.ipynb
Delves into various software testing techniques, both manual and automated.
- Functional Testing: Unit, Integration, System, and Acceptance Testing
- Non-Functional Testing: Performance, Load, Security, and Usability Testing
- Writing and Analyzing Test Cases
Outcome: Equips developers with skills to ensure robust and reliable Python applications.
3. Parallelism & Concurrency
File: parallelism_zero_to_mastery.ipynb
Explores concurrency and parallelism in Python with practical examples.
- Concurrency with
asyncio: Queues, Tasks, and Synchronization - Threading for Concurrency
- Multiprocessing for Parallelism
- Comparing Threading vs. Multiprocessing
Outcome: Provides insights into writing efficient, parallelized Python programs.
4. Decorators & Metaclasses
File: decorator__zero_to_mastery.ipynb
Deep dive into Python decorators and metaclasses:
- Function Decorators: Basics to Advanced, with Arguments
- Class Decorators and Chaining Decorators
- Common Python Decorators:
@staticmethod,@classmethod,@property,@lru_cache,@dataclass - Metaclasses: Enforcing Class Behavior, Singleton Metaclass
Outcome: Enhances understanding of advanced Python programming patterns for clean and reusable code.
5. Data Serialization
Explores different methods of serializing data in Python:
- Formats: JSON, Pickle, and YAML
- Comparisons of their use cases and limitations
Outcome: Helps developers work efficiently with data serialization in Python.
6. [More to Come…]
This repository is continually evolving, with plans to include more advanced Python topics.
Technologies Used
- Python, Jupyter Notebooks.
Key Takeaways
- Provides a structured approach to mastering Python programming concepts.
- Covers practical use cases and best practices for real-world applications.
- Regularly updated to stay relevant to current Python trends.
Explore the repository here: Python Zero to Mastery