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

About the Authors:

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

Ian is a chief data scientist and coach. He co-organizes the annual PyDataLondon 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 of experience as a senior data science leader, trainer and team coach. For fun he's walked by his high-energy Springer Spaniel, surfs the Cornish coast and drinks fine coffee. Past talks and articles can be found at: https://ianozsvald.com/

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High Performance Python : Practical Performant Programming for Humans

Gorelick, Micha; Ozsvald, Ian

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