Optimization
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When writing code, functions serve as a fundamental concept that enables modular programming and improved maintainability. If you encounter performance bottlenecks with slow execution speeds, function optimization can significantly boost performance. Optimization strategies include implementing more efficient algorithms (such as replacing O(n²) with O(n log n) approaches), reducing unnecessary function calls and parameter passing overhead, and employing caching mechanisms like memoization. When designing functions, adhere to best practices including writing comprehensive docstrings (using formats like Google-style or NumPy-style), implementing proper exception handling with try-except blocks, and validating input parameters to ensure code readability and robustness. Additionally, consider using profiling tools (e.g., cProfile in Python) to identify performance hotspots before optimization.
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