Mastering python high performance

Saved searches

Use saved searches to filter your results more quickly

You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session.

Python High Performance – Second Edition, published by Packt

License

PacktPublishing/Python-High-Performance-Second-Edition

This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?

Sign In Required

Please sign in to use Codespaces.

Launching GitHub Desktop

If nothing happens, download GitHub Desktop and try again.

Launching GitHub Desktop

If nothing happens, download GitHub Desktop and try again.

Launching Xcode

If nothing happens, download Xcode and try again.

Launching Visual Studio Code

Your codespace will open once ready.

There was a problem preparing your codespace, please try again.

Latest commit

Git stats

Files

Failed to load latest commit information.

README.md

If you have read this book, please leave a review on Amazon.com. Potential readers can then use your unbiased opinion to help them make purchase decisions. Thank you. The $5 campaign runs from December 15th 2020 to January 13th 2021.

Python High Performance — Second Edition

This is the code repository for Python High Performance — Second Edition, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

Python is a versatile language that has found applications in many industries. The clean syntax, rich standard library, and vast selection of third-party libraries make Python a wildly popular language.

Python High Performance is a practical guide that shows how to leverage the power of both native and third-party Python libraries to build robust applications.

The book explains how to use various profilers to find performance bottlenecks and apply the correct algorithm to fix them. The reader will learn how to effectively use NumPy and Cython to speed up numerical code. The book explains concepts of concurrent programming and how to implement robust and responsive applications using Reactive programming. Readers will learn how to write code for parallel architectures using Tensorflow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark.

By the end of the book, readers will have learned to achieve performance and scale from their Python applications.

Instructions and Navigation

All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.

Chapter 9 does not contain any code.

The code will look like the following:

 def square(x): return x * x inputs = [0, 1, 2, 3, 4] outputs = pool.map(square, inputs) 

The software in this book is tested on Python version 3.5 and on Ubuntu version 16.04. However, majority of the examples can also be run on the Windows and Mac OS X operating systems. The recommended way to install Python and the associated libraries is through the Anaconda distribution, which can be downloaded from https://www.continuum.io/downloads, for Linux, Windows, and Mac OS X.

If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.

About

Python High Performance – Second Edition, published by Packt

Источник

Mastering Python High Performance

Read it now on the O’Reilly learning platform with a 10-day free trial.

O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Book description

Measure, optimize, and improve the performance of your Python code with this easy-to-follow guide

• 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.

• 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.

This easy-to-follow, practical guide will help you enhance your optimization skills by improving real-world code.

Источник

Mastering Python High Performance

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.

You can also get this PDF by using our Android Mobile App directly:

Источник