High Performance Python pdf epub mobi txt 电子书下载 2025

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Micha Gorelick cofounded Fast Forward Labs as resident mad scientist. The company was acquired by Cloudera in 2017. He works on many issues from machine learning to performant stream algorithms.

Ian is a chief data scientist and coach. He co-organizes the annualPyDataLondon conference with 700+ attendees and the associated 10,000+ member monthly meetup. He runs the established Mor Consulting Data Science consultancy in London and gives conference talks internationally, often as keynote speaker. He has 17 years ofexperience as a senior data science leader, trainer and team coach.For fun he's walked by his high-energy Springer Spaniel, surfs theCornish coast and drinks fine coffee. Past talks and articles can befound at: https://ianozsvald.com/

出版者:O'Reilly Media

作者:Micha Gorelick

出品人:

页数:468

译者:

出版时间:2020-5-19

价格:0

装帧:Paperback

isbn号码:9781492055020

丛书系列:

图书标签:

Python
coding
编程
程序效率
数学和计算机
優化

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Your Python code may run correctly, but you need it to run faster. Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By exploring the fundamental theory behind design choices, High Performance Python helps you gain a deeper understanding of Python’s implementation.

How do you take advantage of multicore architectures or clusters? Or build a system that scales up and down without losing reliability? Experienced Python programmers will learn concrete solutions to many issues, along with war stories from companies that use high-performance Python for social media analytics, productionized machine learning, and more.

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

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

Deploy code faster using tools like Docker