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Description

Simply knowing how to code is not enough; on mission-critical pieces of code, every bit of memory and every CPU cycle counts, and knowing how to squish every bit of processing power out of your code is a crucial and sought-after skill. Nowadays, Python is used for many scientific projects, and sometimes the calculations done in those projects require some serious fine-tuning. Profilers are tools designed to help you measure the performance of your code and help you during the optimization process, so knowing how to use them and read their output is very handy. This book starts from the basics and progressively moves on to more advanced topics. You’ll learn everything from profiling all the way up to writing a real-life application and applying a full set of tools designed to improve it in different ways. In the middle, you’ll stop to learn about the major profilers used in Python and about some graphic tools to help you make sense of their output. You’ll then move from generic optimization techniques onto Python-specific ones, going over the main constructs of the language that will help you improve your speed without much of a change. Finally, the book covers some number-crunching-specific libraries and how to use them properly to get the best speed out of them. After reading this book, you will know how to take any Python code, profile it, find out where the bottlenecks are, and apply different techniques to remove them.

What you will learn

Master code optimization stepbystep and learn how to use different tools

Understand what a profiler is and how to read its output

Interpret visual output from profiling tools and improve the performance of your script

Use Cython to create fast applications using Python and C

Take advantage of PyPy to improve performance of Python code

Optimize numbercrunching code with NumPy, Numba, Parakeet, and Pandas

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Python 3 Object-Oriented Programming - Second Edition

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Customer reviews

Rating distribution

4.2

(5 Ratings)

5 star 20%

4 star 80%

3 star 0%

2 star 0%

1 star 0%

Its Me Hara Jun 09, 2018

Good item. Good book for Python developer

Amazon Verified review

Duncan W. Robinson Oct 16, 2015

Though I’ve programmed in R for some time, I am relatively new to Python. “Mastering Python High Performance” helped me with some Python syntax issues. I found Chapter 4 “Optimize Everything”, which includes coverage on dictionary lookups, list comprehensions & miscellaneous tips / tricks, the most useful. Chapter 5 was also interesting as it helped me understand the pros & cons of multithreading versus multiprocessing. Overall, I found this a helpful & informative text for a Python programmer with some prior experience in the language.

Amazon Verified review

Mike Driscoll Oct 15, 2015

When I was originally reading this book, I was interested in seeing how the author would speed up their code. He covers a wide variety of topics which is good, but that also hampers the book as none of the topics are covered in depth. He uses lots of small examples and shows how to profile before moving on to optimizing them. Let’s go over each of the chapters so you can get a taste of what the book covers. Full disclosure: I was a technical reviewer of the book for Packt.Chapter one is all about profiling your code. It covers the differences between statistical and event-based profiling, what profiling is and why it’s important, bottlenecks and memory leaks. It also goes over running time complexity (linear, factorial, quadratic, etc) and profiling best practices.Then we logically move into chapter two where we learn about some profilers we can use with Python. The two that are covered are cProfile (included with Python) and line_profiler. The author demonstrates various ways to use cProfile to measure your code and also how to use Python’s pstats module, which is used for analyzing the results you receive from cProfile. Then the author moves on to using line_profiler and kernprof to analyze the same (or similar) examples used with cProfile. I think this is one of the best balanced chapters in the book and really quite interesting all by itself.Chapter three goes into using visual tools to help you understand your profiler’s output. In this chapter, you will learn about KCacheGrind / pyprof2calltree and RunSnakeRun. For the most part, you’ll just learn how to use these tools to figure out what your data means.In chapter four, we learn about optimizing your code. Topics covered are memoization, list comprehensions, generators, ctypes, loading your own custom C library, and some others tips and tricks. While this chapter is short, it has some good ideas and is worth reading or at least skimming.Chapter five digs into multithreading and multiprocessing. You’ll learn about the pros and cons of each. You will also learn about the Global Interpreter Lock and how that affects you when you choose one of these techniques.Chapter six goes into using PyPy and Cython and how they can be useful for additional optimizations to your code. I enjoyed this chapter, although I didn’t feel that PyPy got as much attention as Cython. There also weren’t very many coding examples.If you’re into number crunching, then chapter seven is for you. It goes over how to use Numba, Parakeet and pandas. Quite frankly, out of the three of libraries, I had only ever heard of pandas. I personally don’t need to do a lot of number crunching in my line of work, but it was interesting to see how each of the libraries worked and get a general idea of what they could be used for.Finally in chapter eight, the author attempts to put it all together. This chapter probably should have been twice as long as it is so that he could have actually covered everything. But in the end, it covers just barely enough and you do get to see a full example get optimized from beginning to end.Overall, I enjoyed this book. I would recommend this to anyone who needs ideas for optimizing their Python code or just for learning about profiling in general.

Amazon Verified review

Adriano Ribeiro Jan 23, 2016

I think ththis book is a practical for impatients programmers. For me, the most interesting chapters were 4:Optimize Everything, 6:Generic Optimization Options and 7:Lightning Fast Number Crunching with Numba, Parakeet, and pandas. Very good start point.

Amazon Verified review

Chris Dec 01, 2015

Mastering Python High Performance is a worthwhile buy for python programmers who are looking to take their skills to a new level. It gives you a pretty good explanation of how the code is executed when it is run which can be useful for anyone who wants to have more in depth knowledge of computing. If I had one gripe it would be it seems to rush into the topic a bit too quickly and jumps into algorithm and doesn’t sufficiently go over the topic. Overall, “High Performance Python” is a vast topic and this book does a good enough job getting into the finer points of python coding.

Amazon Verified review

Mastering Python High Performance: Learn how to optimize your code and Python performance with this vital guide to Python performance profiling and benchmarking

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