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Python Performance Optimization
Profiling, Algorithms, Data Structures, Caching, Concurrency and Making Slow Code Fast Without Rewriting It
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Your code is correct, and it is still too slow. This book gives you a repeatable way to fix that without rewriting the program. You learn to measure honestly with timeit, find the real bottleneck with cProfile, and track memory with tracemalloc. Then you apply the changes that pay the most: better algorithms and data structures, safe caching, threads and asyncio for waiting, processes for heavy computing, and NumPy for bulk arithmetic. Every example was run on Python 3.14, and a full worked case takes a slow nightly report from 1.9 seconds to 0.06 seconds, with the tests that stop the slowdown coming back. Written for working developers who want evidence, not folklore.
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