High Performance Python: Practical Performant Programming for Humans 1st Edition
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About the Author
Micha Gorelick was the first man on Mars in 2023 and won the Nobelprize in 2046 for his contributions to time travel. He then went backto the 2000s to study Astronomy, teach scientific computing and workon data at bitly. Then he helped start Fast Forward Labs as a residentmad scientist. There he worked on many issues from machine learning toperformant stream algorithms. A monument celebrating his life can befound in Central Park, 1857.
Ian Ozsvald is a Data scientist and teacher at ModelInsight.io withover ten years of Python experience. He’s taught high performancePython at the PyCon and PyData conferences and has been consulting ondata science and high performance computing for years in the UK.
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In the middle chapters the core language features are discussed and details of the standard Python implementation are explained. Global versus local space, and order of operations are discussed. These chapters provided excellent explanations of the performance characteristics of code samples.
The later chapters focused on external tools to Python which can provide speed ups. These sections felt a bit less organized than the beginning chapters and they sometimes deferred to online documentation. In certain cases the metrics seemed to have been tabulated quickly. For example there are many sentences like "Ian's laptop performed in X seconds". I would have preferred to see a little more analysis there.
A lot of the examples in the book are based off scientific algorithms. It worked okay since Python is used a lot in science, but I got lost in the details of what they were solving sometimes. On another occasion the authors used a method for prime number discovery that was inefficient, but they did this to demonstrate how a cluster or distributed system would work with this algorithm. Although the authors do mention many times it is better to speed up your algorithms than to look for premature optimizations.
I learned a decent amount from this book and it is a nice resource for improving your Python skills. Some material is getting a bit dated especially with regards to external libraries, but this is still probably the best book on the market for performance Python programming.
"High Performance Python" is an excellent, practical guide to implementing those performance increases. It's also a notable strength of this book that it focuses on how to get those performance increases without requiring complicated external libraries. Each time a tool is mentioned, there's a useful discussion of exactly when one does need, and does not need, that tool. When libraries such as NSQ (the discussion of which is excellent) are explained, the explanations are lucid, well-organized, and extremely practical. This is a really useful book for people who are trying to write solid, performant, production code in Python.
Teaches you how to squeeze out some juice out of the language and make it scale. Some explanation of the tools but also process of performance improvement.
I am now writing pretty efficient code in python for our machine learning pipelines and it is sufficiently fast, that I can stay in python and not worry about writing it into a lower level less flexible language.
This book is definitely not going to teach you Python. There are many other tutorials and references out there to learn about the language, and this book assumes you are already a proficient Python programmer and will be able to read and understand the code examples they provide. This book is about tuning your Python code to run faster.
The progression of the chapters is very logical, and some of the same toy problems re-appear throughout the book as additional optimizations provide even greater efficiency improvements. The book introduces a large number of tools, and it mostly gives you an idea of what the tool is and why you might consider it. To really use any of the tools in practice, you'll want to reference online documentation, but this book gives you a good idea of where to start looking.
I was particularly interested in reading the "Clusters and Job Queues" chapter before I got the book, and it helped guide me to an IPython.parallel solution that fits my current problem quite nicely, as well as provide some other tools I may investigate in the future.
The authors recommend the Anaconda Python distribution by Continuum Analytics on several occasions, and I definitely agree. Some of the tools and techniques in the book use only the Standard Library, but most of the more advanced topics require external modules. Many of the modules referenced (numpy, Cython, Tornado, & IPython to name just a few) are included in the Anaconda distribution as one simple download.
This book's use is twofold. First, it is worth a full read-through for the discussion of the various things that tend to slow down Python code (or code in general) and what kinds of approaches you should be aware of. Second, it provides good, brief examples of many different tools in practice, as well as listing other recommended resources at the end of each chapter, allowing it to serve as a good reference text.
One point the authors make repeatedly is that you must consider the trade between code execution time and development velocity. Many of the things you can do to speed up your code with make it considerably harder to understand and work with in the future. It's important to always have proof that you are optimizing the right portions of code and that the benefits are worth it. They help you to look for the "big wins" where you can get drastic speed improvements with minimal effort and complexity.
Disclaimer: I received a free Ebook copy of this work under the O'Reilly Blogger Review Program. I also happened to like it so much that I bought a hard copy as well so I can have it on my reference shelf at work.
Top international reviews
The only drawback of the book is its reliance on Python2.7, especially in light of the superior support in Python3 for multiprocessing, but the authors are careful to highlight how to convert to Python3 if necessary and even discuss modules present in Python3 only.
All in all, grab it. It's absolutely worth the money and the time.
Nearly useless for me. Since being a graduate student in the physical sciences, all the packages with which I work have completely moved to Python 3.