SKU/Artículo: AMZ-B0FFSMF8JL
Mastering Performant Code: Efficiency, Profiling and Data Structures in Python
Format:
Paperback
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
1.59 kg
1.59 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Mastering Performant Code in Python is a hands-on blueprint for seasoned Python developers who want to go beyond theory and actually build the data-structure and optimisation skills the job market rewards. If you can read a Big-O graph, write a class, and run a unit test, this book picks up from there and takes you all the way to production-ready, profiled, and benchmarked code.Why this book?Implementation-first: every concept is introduced by writing it, testing it, timing it. You don’t just read about AVL trees or Bloom filters—you ship them, with type hints and 100 % test coverage .Performance obsession: each chapter ends with side-by-side speed and memory tables so you can see exactly when a hand-rolled structure outpaces a Python built-in .Real-world focus: text-editor buffers, in-memory DBs and caching layers show up as worked examples, proving the techniques survive outside the REPL .What you’ll masterCPython internals—how lists resize, how dict hashing really works, and the memory layout that makes some operations O(1)O(1) and others O(n)O(n) .Fifteen+ data structures built from scratch, from dynamic arrays through balanced trees to probabilistic filters, each wrapped in modern Python idioms (dataclasses, context managers, mypy-friendly types) .A profiler’s toolbox: timeit, cProfile, tracemalloc, plus statistical benchmarking harnesses you can drop into any codebase .Production optimisation moves—__slots__, object pools, Cython fall-backs, and a full deployment pipeline that bakes in performance tests and CI/CD hooks .How you’ll learnA repeatable seven-step chapter pattern (Motivation → Theory → Implementation → Tests → Benchmarks → Applications → Exercises) keeps the pace brisk yet structured .Over fifty graded exercises—many open-ended—push you to tweak growth factors, hunt memory leaks, and make thread-safe variants until the knowledge sticks .Zero external dependencies: the entire journey runs on the standard library so you spend time learning fundamentals, not wrangling installs .By the final page you’ll have a personal toolbox of battle-tested data structures, the instinct to profile before you guess, and the confidence that comes from watching your code outrun the stock implementations. If your next milestone is a system that has to stay fast at scale—or an interview where “implement an LRU cache” is just the warm-up—Mastering Performant Code in Python will get you there.
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AR$51.500
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AR$26.414
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