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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.\r\n\r\n## Topics\r\n\r\n### Testing\r\n- [x] Unit testing with `unittest`\r\n- [x] Mocking and patching\r\n- [ ] Test-driven development (TDD)\r\n- [ ] Using `pytest` for advanced testing\r\n- [ ] Property-based testing with `hypothesis`\r\n\r\n### Automation\r\n- [ ] Automating tasks with `os` and `shutil`\r\n- [ ] Scripting with `argparse` and `click`\r\n- [ ] Automating web interactions with `selenium`\r\n- [ ] Scheduling tasks with `apscheduler`\r\n\r\n### Metaprogramming\r\n- [ ] Understanding `type` and `metaclass`\r\n- [x] Using `__getattr__`, `__setattr__`, and `__delattr__`\r\n- [ ] Creating decorators and function wrappers\r\n- [ ] Dynamic class creation and modification\r\n\r\n### Memory Management\r\n- [ ] Reference counting and garbage collection\r\n- [ ] The `gc` module\r\n- [ ] Weak references and the `weakref` module\r\n- [ ] Analyzing memory usage with `memory_profiler`\r\n\r\n### Asyncio\r\n- [ ] Basics of asynchronous programming\r\n- [ ] Coroutines, tasks, and the event loop\r\n- [ ] Using `asyncio` for I/O-bound tasks\r\n- [ ] Combining `asyncio` with `aiohttp` and `asyncpg`\r\n- [ ] Advanced `asyncio` patterns\r\n\r\n### Concurrency\r\n- [ ] Thread-based concurrency with `threading`\r\n- [ ] Process-based concurrency with `multiprocessing`\r\n- [ ] Using `concurrent.futures` for simpler parallelism\r\n- [ ] Understanding the Global Interpreter Lock (GIL)\r\n\r\n### Parallelism\r\n- [ ] Parallel processing with `multiprocessing`\r\n- [ ] Distributed computing with `dask`\r\n- [ ] Leveraging GPUs with libraries like `cupy`\r\n\r\n### CPython Internals\r\n- [ ] Understanding Python's execution model\r\n- [ ] Exploring Python bytecode with `dis`\r\n- [ ] Writing C extensions for Python\r\n- [ ] Exploring the Python C API\r\n\r\n### Data Engineering\r\n- [ ] Data serialization with `pickle` and `json`\r\n- [ ] Working with large datasets using `pandas` and `dask`\r\n- [ ] ETL pipelines with `airflow`\r\n\r\n### Advanced Python Libraries\r\n- [ ] NumPy for numerical computing\r\n- [ ] Advanced plotting with Matplotlib and Seaborn\r\n- [ ] Scikit-learn for machine learning\r\n- [ ] TensorFlow and PyTorch for deep learning\r\n\r\n### Debugging and Profiling\r\n- [ ] Using `pdb` and `ipdb` for debugging\r\n- [ ] Profiling with `cProfile` and `line_profiler`\r\n- [ ] Tracing and logging with `trace` and `logging`\r\n\r\n### Performance Optimization\r\n- [ ] Writing efficient code with `cython`\r\n- [ ] Vectorization with `numpy`\r\n- [ ] Just-in-time compilation with `numba`\r\n- [ ] Using `lru_cache` for caching results\r\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsccsmartcode%2Fadvanced_python_programming","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsccsmartcode%2Fadvanced_python_programming","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsccsmartcode%2Fadvanced_python_programming/lists"}