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High Performance Python: Practical Performant Programming for Humans 2nd Edition, Kindle Edition

by Micha Gorelick (Author), Ian Ozsvald (Author) Format: Kindle Edition
4.7 4.7 out of 5 stars 177 ratings
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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
Previous page
  1. ISBN-13
    978-1492055020
  2. Edition
    2nd
  3. Sticky notes
    On Kindle Scribe
  4. Publisher
    O'Reilly Media
  5. Publication date
    April 30, 2020
  6. Language
    English
  7. File size
    12161 KB
  8. See all details
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  2. Introducing Python: Modern Computing in Simple Packages
    Python in a Nutshell: A Desktop Quick Reference
    Fluent Python: Clear, Concise, and Effective Programming
    Robust Python: Write Clean and Maintainable Code

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From the Publisher

High Performance Python

From the Preface

Python is easy to learn. You’re probably here because now that your code runs correctly, you need it to run faster. You like the fact that your code is easy to modify and you can iterate with ideas quickly. The trade-off between easy to develop and runs as quickly as I need is a well-understood and often-bemoaned phenomenon. There are solutions.

Some people have serial processes that have to run faster. Others have problems that could take advantage of multicore architectures, clusters, or graphics processing units. Some need scalable systems that can process more or less as expediency and funds allow, without losing reliability. Others will realize that their coding techniques, often borrowed from other languages, perhaps aren’t as natural as examples they see from others.

In this book we will cover all of these topics, giving practical guidance for understanding bottlenecks and producing faster and more scalable solutions. We also include some war stories from those who went ahead of you, who took the knocks so you don’t have to.

Python is well suited for rapid development, production deployments, and scalable systems. The ecosystem is full of people who are working to make it scale on your behalf, leaving you more time to focus on the more challenging tasks around you.

Who This Book Is For

You’ve used Python for long enough to have an idea about why certain things are slow and to have seen technologies like Cython, numpy, and PyPy being discussed as possible solutions. You might also have programmed with other languages and so know that there’s more than one way to solve a performance problem.

While this book is primarily aimed at people with CPU-bound problems, we also look at data transfer and memory-bound solutions. Typically, these problems are faced by scientists, engineers, quants, and academics.

We also look at problems that a web developer might face, including the movement of data and the use of just-in-time (JIT) compilers like PyPy and asynchronous I/O for easy-win performance gains.

It might help if you have a background in C (or C++, or maybe Java), but it isn’t a prerequisite. Python’s most common interpreter (CPython—the standard you normally get if you type python at the command line) is written in C, and so the hooks and libraries all expose the gory inner C machinery. There are lots of other techniques that we cover that don’t assume any knowledge of C.

You might also have a lower-level knowledge of the CPU, memory architecture, and data buses, but again, that’s not strictly necessary.

Who This Book Is Not For

This book is meant for intermediate to advanced Python programmers. Motivated novice Python programmers may be able to follow along as well, but we recommend having a solid Python foundation.

We don’t cover storage-system optimization. If you have a SQL or NoSQL bottleneck, then this book probably won’t help you.

Editorial Reviews

About the Author

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/

Product details

  • ASIN ‏ : ‎ B087YTVL8F
  • Publisher ‏ : ‎ O'Reilly Media; 2nd edition (April 30, 2020)
  • Publication date ‏ : ‎ April 30, 2020
  • Language ‏ : ‎ English
  • File size ‏ : ‎ 12161 KB
  • Simultaneous device usage ‏ : ‎ Unlimited
  • Text-to-Speech ‏ : ‎ Enabled
  • Enhanced typesetting ‏ : ‎ Enabled
  • X-Ray ‏ : ‎ Not Enabled
  • Word Wise ‏ : ‎ Not Enabled
  • Sticky notes ‏ : ‎ On Kindle Scribe
  • Print length ‏ : ‎ 744 pages
  • Customer Reviews:
    4.7 4.7 out of 5 stars 177 ratings

About the authors

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Previous page
  1. Micha Gorelick

    Micha Gorelick

    Brief content visible, double tap to read full content.
    Full content visible, double tap to read brief content.

    Micha Gorelick was the first woman on Mars in 2033 and won the Nobel Prize in 2056 for her contributions to time travel. After seeing the deplorable uses of her new technology, she traveled back in time to 2012 and convinced herself to quit her nascent research into time travel and follow her love of data. She has since cofounded Fast Forward Labs, an applied machine learning research lab, authored multiple papers on ethical computing and helped build the inclusive community space Community Forge in Wilkinsburg. In 2019 she cofounded Probable Models, an ethical machine learning group, which made the interactive immersive play "Project Amelia". In 2020 she can be found in France helping journalists at the OCCRP find stories in data. A monument celebrating her life can be found in Central Park, 1857.

    See more on the author's page
  2. Ian Ozsvald

    Ian Ozsvald

    Brief content visible, double tap to read full content.
    Full content visible, double tap to read brief content.

    Ian is a Chief Data Scientist and Coach, he co-organises the annual PyDataLondon conference with 700+ attendees and the associated 11,000+ member monthly meetup. He runs the established Mor Consulting Data Science consultancy in London, gives conference talks internationally often as keynote speaker and is the author of the bestselling O'Reilly book High Performance Python (2nd edition). 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/

    See more on the author's page
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Customer reviews

4.7 out of 5 stars
4.7 out of 5
177 global ratings

Top reviews from the United States

Michelle
5.0 out of 5 stars Great book
Reviewed in the United States on October 24, 2023
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Good read
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Pen Name
5.0 out of 5 stars Excellent book
Reviewed in the United States on January 23, 2021
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This book is a hidden gem. It explains in-depth methods for how to profile your Python programs, how to use different compilers for python code to gain speedups, how your code gets interpreted and how it interacts with the underlying computer architecture, how to write async code and parallel code in python, how to conserve RAM and many more topics and concepts. Each one of the concepts that this book teaches had a lot of work and effort put into by the authors. Wether it's the text or the code examples, the quality is high. The book is written in a way that you can read it in a linear fashion but it can also serve as a reference once you know where everything is. The authors of this book are very talented and experienced python developers with background in data science. In addition to showing many advanced methods for making your code fast, they put a strong emphasis on when/where to use each method and the importance of profiling and benchmarking.

This book is probably a better read for people with an intermediate understanding of Python, operating systems and parallel programming. It uses very interesting and high quality problems as examples (warning, they are not always easy to understand). I highly recommend this book to anyone who has been using Python for a few years and wants to take their understanding of the language to the next level.
7 people found this helpful
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Vlad Bezden
5.0 out of 5 stars All you need to know about performance and memory optimization in Python
Reviewed in the United States on May 20, 2022
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A very comprehensive book that covers the memory and performance of Python. Even though the book's title contains "performance," it also has a chapter on memory management. Book has excellent and up to the point examples.
One person found this helpful
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W
4.0 out of 5 stars Issues with the cover
Reviewed in the United States on July 10, 2021
The cover should be a Reticulated Python. They are the highest performance python.
13 people found this helpful
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Jason in Chicago
5.0 out of 5 stars Good book
Reviewed in the United States on January 8, 2021
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I’m learning a lot and half way through the book. I’ve written some python before but primarily an expert C++ programmer. Ironically I ordered a more fundamental python book and it’s stuck in the mail but still finding this book very understandable.
One person found this helpful
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Silverbox
3.0 out of 5 stars Good information, way too long, weird writing style
Reviewed in the United States on August 9, 2021
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Good: The book does a nice job covering multiple aspects of speeding up Python code, as well as reducing memory usage, etc. Good examples with test results of different approaches.

Not so good: The book is probably twice as long as it should be. There's a ton of filler in here, like the odd opinion notes about hard it is moving code to a cluster, or using multiprocessing. The final section appears to be random thoughts about Python code, and has nothing to do with performance.

Also, the writing style is just simply weird. I was reading along about some topic, and all of a sudden the text says, "Ian doesn't like to have his laptop set up so that ...". I thought, "Who is Ian?, and why would I care about his opinion?" Well, it turns out he's one of the two authors. This approach is used multiple times, and boy is it jarring - such a weird way of writing, especially in a technical book.

That said, if you can wade through the strange writing and ignore all the useless fluff, there is some good content in this.
15 people found this helpful
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Yasuke Miyazawa
5.0 out of 5 stars Very good
Reviewed in the United States on March 13, 2022
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Thank you
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Amazon Customer
4.0 out of 5 stars I just finished this book
Reviewed in Canada on February 25, 2022
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It's a great book with useful information. It will take your understanding of Python's internal workings, memory allocation and how to use better performance libraries. The only down side to this book is I found some of the examples to verbose and unnecessarily complex for the point being proven. Someone who isn't into data science as much may find the example arduous.
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João Silva
5.0 out of 5 stars Learning with it
Reviewed in Spain on March 29, 2022
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Kwik word of advice. Do not expect this to be a easy read. Buy a notebook and some pens because you will be taking notes. If you are a beginner in Python programming wait a year or two before reading this.
One person found this helpful
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robkianta
5.0 out of 5 stars High Performance Python
Reviewed in Italy on July 7, 2021
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Sono ancora sotto le 100 pagine di lettura ma gia' dall' inizio si percepisce una qualita' superiore rispetto ad altri manuali sul python. Utilissimo il sito github per scaricare i sorgenti e fare prove in locale. Pienamente soddisfatto.
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BlueBrain
2.0 out of 5 stars Another Bogus Book by O'REILLY and SPD
Reviewed in India on December 22, 2020
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There was a time when this publisher's books were considered the most authoritative ones. And now for the past 5-6 years I am watching that worst and most superficial books are being released from this publisher. The have become sort of guaranteed of worst books on the topic where they write a lot from here there, try to cover lots of topic with almost zero depth. And end up suggesting you to buy a few more OReilly books, from where also you hardly get anything.
This book was supposed to be on High performance computing with python, but they have written a chapter of Multiprocessing module, and without explaining it, they job explaining joblib module! You will see the chapter of Multiprocessing long enough, but it hardly gives any proper introduction and detailing. They don't even explain their codes point by point.
Also the writing style looks incoherent. Some chapters look a bit better written while you feel that some chapters are written by some kid. No depth at all. They did not even cover properly or say anything on important topics like Futures.
Entire book looks trying to impress you with bringing names of modules only with hardly any meaningful detailing. I am almost on the brink of stopping buying SPD and Oreilly books. They r just behind money and publishing junks, that too overpriced.
13 people found this helpful
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roman575
5.0 out of 5 stars excellent high quality material
Reviewed in the United Kingdom on June 30, 2020
Verified Purchase
Authors have done excellent job compiling the material, running performance tests, digging into details.
One person found this helpful
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