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Mastering Python High Performance, by Fernando Doglio
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Measure, optimize, and improve the performance of your Python code with this easy-to-follow guide
About This Book- Master the do's and don'ts of Python performance programming
- Learn how to use exiting new tools that will help you improve your scripts
- A step-by-step, conceptual guide to teach you how to optimize and fine-tune your critical pieces of code
If you're a Python developer looking to improve the speed of your scripts or simply wanting to take your skills to the next level, then this book is perfect for you.
What You Will Learn- Master code optimization step-by-step 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 number-crunching code with NumPy, Numba, Parakeet, and Pandas
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.
Style and approachThis easy-to-follow, practical guide will help you enhance your optimization skills by improving real-world code.
- Sales Rank: #2551744 in Books
- Published on: 2015-09-09
- Released on: 2015-09-09
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .59" w x 7.50" l, 1.00 pounds
- Binding: Paperback
- 260 pages
About the Author
Fernando Doglio
Fernando Doglio has been working as a web developer for the past 10 years. During that time, he shifted his focus to the Web and grabbed the opportunity of working with most of the leading technologies, such as PHP, Ruby on Rails, MySQL, Python, Node.js, AngularJS, AJAX, REST APIs, and so on. In his spare time, Fernando likes to tinker and learn new things. This is why his GitHub account keeps getting new repos every month. He's also a big open source supporter and tries to win the support of new people with the help of his website, lookingforpullrequests.com. You can reach him on Twitter at @deleteman123. When he is not programming, he spends time with his family.
Most helpful customer reviews
3 of 3 people found the following review helpful.
He covers a wide variety of topics which is good, but that also hampers the book as none ...
By Mike Driscoll
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.
1 of 1 people found the following review helpful.
the most useful. Chapter 5 was also interesting as it helped ...
By DWR
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.
0 of 0 people found the following review helpful.
"High Performance Python” is a vast topic and this book does a good enough job getting into the finer points of python coding
By Chris
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.
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