Python Performance Engineering: Strategies and Patterns for Optimized Code
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- FormatePub
- ISBN8230828785
- EAN9798230828785
- Date de parution10/04/2025
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurIndependently Published
Résumé
"High Performance Python: Practical Performant Programming for Humans" is a comprehensive guide that helps Python developers optimize their code for better speed and memory efficiency. Written by Micha Gorelick and Ian Ozsvald, this book explores fundamental performance theory while providing …
"High Performance Python: Practical Performant Programming for Humans" is a comprehensive guide that helps Python developers optimize their code for better speed and memory efficiency. Written by Micha Gorelick and Ian Ozsvald, this book explores fundamental performance theory while providing practical solutions to common bottlenecks. It covers essential topics including profiling techniques, data structure optimization, memory management, concurrency, and parallelism.
The book is particularly valuable for intermediate to advanced Python developers who need their code to run faster in high-data-volume programs. It includes real-world examples and "war stories" from companies using high-performance Python for applications like social media analytics and machine learning. Readers appreciate its methodological approach to optimization: isolate, profile, and optimize specific parts of a program.
Beyond just teaching optimization techniques, the book provides insight into Python's internal workings and introduces readers to powerful tools like Cython, NumPy, and PyPy. While primarily focused on Python 2.7 in earlier editions, it covers concepts applicable to modern Python versions.
The book is particularly valuable for intermediate to advanced Python developers who need their code to run faster in high-data-volume programs. It includes real-world examples and "war stories" from companies using high-performance Python for applications like social media analytics and machine learning. Readers appreciate its methodological approach to optimization: isolate, profile, and optimize specific parts of a program.
Beyond just teaching optimization techniques, the book provides insight into Python's internal workings and introduces readers to powerful tools like Cython, NumPy, and PyPy. While primarily focused on Python 2.7 in earlier editions, it covers concepts applicable to modern Python versions.