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Python High Performance – Second Edition, published by Packt

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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.

About the Book

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.

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