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High Performance Python. Practical Performant Programming for Humans. 3rd Edition (2025) [PDF, EPUB]

Micha Gorelick
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Серия
Бестселлеры O'Reilly (книга #193)
Издатель
Дата публикации
29.04.2025 00:00
Добавлена в библиотеку
17.05.2026 16:25
Последнее изменение
30.11.-0001 00:00
Форматы
EPUB (8760 KB), PDF (10085 KB)
Путь в библиотеке
Micha Gorelick/High Performance Python. Practical (12737)
UUID
12d40aac-8fdd-488f-af0b-d139276dd356

Аннотация

Micha Gorelick, Ian Ozsvald | High Performance Python. Practical Performant Programming for Humans. 3rd Edition (2025) [PDF, EPUB]

picАвтор: Micha Gorelick, Ian Ozsvald

Издательство: O’Reilly Media

ISBN: 978-1098165963

Жанр: Computer Performance Optimization, Python Programming, Software Development

Язык: Английский

 

Формат: PDF, EPUB

Качество: Изначально электронное (ebook)

Иллюстрации: Цветные и черно-белые

 

Описание:

Your Python code may run correctly, but what if you need it to run faster? This practical book shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By explaining the fundamental theory behind design choices, this expanded edition of High Performance Python helps experienced Python programmers gain a deeper understanding of Python's implementation.

 

How do you take advantage of multicore architectures or compilation? Or build a system that scales up beyond RAM limits or with a GPU? Authors Micha Gorelick and Ian Ozsvald reveal concrete solutions to many issues and include war stories from companies that use high-performance Python for GenAI data extraction, productionized machine learning, and more.

 

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Дополнительная информация:

Get a better grasp of NumPy, Cython, and profilers

Learn how Python abstracts the underlying computer architecture

Use profiling to find bottlenecks in CPU time and memory usage

Write efficient programs by choosing appropriate data structures

Speed up matrix and vector computations

Process DataFrames quickly with Pandas, Dask, and Polars

Speed up your neural networks and GPU computations

Use tools to compile Python down to machine code

Manage multiple I/O and computational operations concurrently

Convert multiprocessing code to run on local or remote clusters