High Performance Python
Your Python code may run correctly, but you need it to run faster. By exploring the fundamental theory behind design choices, the updated edition of this practical guide, expanded and enhanced for Python 3, helps you gain a deeper understanding of Python's implementation. You'll learn how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. How can you take advantage of multicore architectures or clusters? Or build a system that can scale up and down without losing reliability? Experienced Python programmers and students alike will learn concrete solutions to these and other issues, along with war stories from companies that use high-performance Python for social media analytics, productionized machine learning, and other situations. Get a better grasp of NumPy, Cython, and profilers Learn how Python abstracts the underlying computer architecture Understand performant pandas Use profiling to find bottlenecks in CPU time and memory usage Write efficient programs by choosing appropriate data structures Speed up matrix, vector, and even tensor computations Use tools to compile Python down to machine code, on CPUs and GPUs Manage multiple I/O and computational operations concurrently Convert multiprocessing code to run on a local or remote cluster Deploy code faster using tools like Docker Solve large problems while using less RAM Get real-life stories and lessons from Python programmers
Stats for nerds
8recommend
22mentions
13people
2014–2025years
36% recommend64% neutral
Mentions per year
What Hacker News says
- This has been an absolute game changer: https://github.com/pytries/marisa-trie (succinct trie with fast lookup and minimal RAM) Use case and more details in the wonderful book by Ian and Micha: High Performance Python
- I still remember discovering & using numba for the first time in university. At the time I didn't know any programming languages except for Python, and wasn't super great at Python either. We had to write and run molecular simulations for our numerical methods and simulations class, and people were writing their code in C/C++ and were blazing through it. I remember finding out about Numba through the High Performance Python O'Reilly book, and by just adding a single @jit decorator, my simulations were dramatically faster.
Amazing stuff, honestly felt like black magic at the time.
- * Effective Python
This teaches 90 specific ways to be more Pythonic
* Robust Python
This teaches some architectural / implementation patterns for code bases larger than a script
* High Performance Python
How to get the most juice out of Python, including compiling
- That is great -- I had to cover the snake on "High Performance Python" when I would read it out loud for my 5 y.o. as he fell asleep.
- High Performance Python is a good book. https://www.oreilly.com/library/view/high-performance-python...
- Not sure if it counts, but High Performance Python[0] starts you out from the bottom explaining the hardware and how it has to process your code, and they go from there. It's a really good read, was recommended by a coworker and I haven't had issues reading through it so far. Also you might learn things to consider when writing or refactoring Python code that affect performance (I always suggest never to "solve" a performance issue that isn't there yet but to instead write something that works first, then try to optimize the code once it works).
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