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https://github.com/sccsmartcode/advanced_python_programming

Explore the depths of Python with advanced concepts like metaprogramming, memory management, asyncio, concurrency, CPython internals, testing, and automation. Learn, practice, and master Python’s full potential
https://github.com/sccsmartcode/advanced_python_programming

advanced-python asyncio concurrency cpython data-engineering debugging memory-management metaprogramming networking optimization parallelism profiling python python-automation python-testing

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Explore the depths of Python with advanced concepts like metaprogramming, memory management, asyncio, concurrency, CPython internals, testing, and automation. Learn, practice, and master Python’s full potential

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README

          

# Advanced Python Programming Learning Repository

Welcome to the **Advanced Python Programming Learning Repository**! This repository is structured to help you explore and track your progress on advanced Python concepts. Each topic includes checkboxes so you can monitor your learning journey.

## Topics

### Testing
- [x] Unit testing with `unittest`
- [x] Mocking and patching
- [ ] Test-driven development (TDD)
- [ ] Using `pytest` for advanced testing
- [ ] Property-based testing with `hypothesis`

### Automation
- [ ] Automating tasks with `os` and `shutil`
- [ ] Scripting with `argparse` and `click`
- [ ] Automating web interactions with `selenium`
- [ ] Scheduling tasks with `apscheduler`

### Metaprogramming
- [ ] Understanding `type` and `metaclass`
- [x] Using `__getattr__`, `__setattr__`, and `__delattr__`
- [ ] Creating decorators and function wrappers
- [ ] Dynamic class creation and modification

### Memory Management
- [ ] Reference counting and garbage collection
- [ ] The `gc` module
- [ ] Weak references and the `weakref` module
- [ ] Analyzing memory usage with `memory_profiler`

### Asyncio
- [ ] Basics of asynchronous programming
- [ ] Coroutines, tasks, and the event loop
- [ ] Using `asyncio` for I/O-bound tasks
- [ ] Combining `asyncio` with `aiohttp` and `asyncpg`
- [ ] Advanced `asyncio` patterns

### Concurrency
- [ ] Thread-based concurrency with `threading`
- [ ] Process-based concurrency with `multiprocessing`
- [ ] Using `concurrent.futures` for simpler parallelism
- [ ] Understanding the Global Interpreter Lock (GIL)

### Parallelism
- [ ] Parallel processing with `multiprocessing`
- [ ] Distributed computing with `dask`
- [ ] Leveraging GPUs with libraries like `cupy`

### CPython Internals
- [ ] Understanding Python's execution model
- [ ] Exploring Python bytecode with `dis`
- [ ] Writing C extensions for Python
- [ ] Exploring the Python C API

### Data Engineering
- [ ] Data serialization with `pickle` and `json`
- [ ] Working with large datasets using `pandas` and `dask`
- [ ] ETL pipelines with `airflow`

### Advanced Python Libraries
- [ ] NumPy for numerical computing
- [ ] Advanced plotting with Matplotlib and Seaborn
- [ ] Scikit-learn for machine learning
- [ ] TensorFlow and PyTorch for deep learning

### Debugging and Profiling
- [ ] Using `pdb` and `ipdb` for debugging
- [ ] Profiling with `cProfile` and `line_profiler`
- [ ] Tracing and logging with `trace` and `logging`

### Performance Optimization
- [ ] Writing efficient code with `cython`
- [ ] Vectorization with `numpy`
- [ ] Just-in-time compilation with `numba`
- [ ] Using `lru_cache` for caching results