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Python Fundamentals: arrays
- Arrays in Python can be handled with built-in lists, the array module, and numpy for optimized numerical operations and performance.
- Real-world use cases include fast API request handling, async job queues, type-safe data models, CLI tools, and ML preprocessing using numpy arrays.
- Integration with Python tooling involves specifying dependencies in pyproject.toml and utilizing tools like mypy for type safety.
- Code examples showcase pre-allocation, vectorized operations with numpy, and considerations for using dataclasses with numpy arrays.
- Failure scenarios and debugging involve handling shape mismatches, using tools like pdb, cProfile, and memory_profiler, and ensuring proper deallocation of numpy arrays.
- Performance and scalability considerations include benchmarking, avoiding unnecessary allocations, controlling concurrency, and using C extensions for critical operations.
- Security considerations highlight the importance of secure deserialization, validating array shapes, and avoiding untrusted input with numpy.fromstring.
- Testing involves unit tests with pytest, property-based testing with hypothesis, and integrating type validation with mypy into CI pipelines.
- Common pitfalls and anti-patterns to avoid include repeated appending to lists, ignoring array shapes, unnecessary type conversions, and lack of vectorization.
- Best practices emphasize type-safety, separation of concerns, defensive coding, modularity, configuration layering, dependency injection, automation, reproducible builds, and documentation.
- Mastering array handling in Python is crucial for scalable systems, performance optimization, and avoiding common pitfalls by following best practices and leveraging numpy.
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