What is Python's performance optimization techniques?
Answer
Python code optimization covers multiple levels. Algorithmic: choose correct data structures (set for membership O(1) vs list O(n), deque for O(1) head operations). Built-ins: built-in functions (implemented in C) are faster than Python equivalents — use sum(), map(), sorted(). List comprehensions are faster than equivalent for-loops. Generator expressions for memory efficiency. Local variables are faster than global/attribute access — cache frequently accessed attributes in local vars. String joining: use "".join(list) not += in loops. __slots__ for memory-heavy classes. lru_cache/cache for memoization. NumPy for array operations (vectorized C operations). Cython/mypyc: compile Python to C. PyPy: JIT-compiled Python for 5-10x faster execution. Profile before optimizing: cProfile, line_profiler, memory_profiler.