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This article presents ten practical Python performance optimization techniques—including list comprehensions, built‑in functions, generator expressions, set lookups, multiprocessing, caching, itertools, Cython/Numba, and profiling—to help developers write faster, more efficient code for large data and CPU‑intensive tasks.
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