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Key benefits

Identify the bottlenecks in your applications and solve them using the best profiling techniques

Write efficient numerical code in NumPy, Cython, and Pandas

Adapt your programs to run on multiple processors and machines with parallel programming

Description

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.

What you will learn

Write efficient numerical code with the NumPy and Pandas libraries

Use Cython and Numba to achieve native performance

Find bottlenecks in your Python code using profilers

Write asynchronous code using Asyncio and RxPy

Use Tensorflow and Theano for automatic parallelism in Python

Set up and run distributed algorithms on a cluster using Dask and PySpark

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

Frequently bought together

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Total Can$ 173.97

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Dimitri Shvorob Feb 23, 2018

Thinking how to rate this book has been a bit of a roller-coaster:Up: "Is this really a Packt book? It's very well written, I like it!"Down: "Hold on - there is another "High-performance Python" book, published by O'Reilly in 2014? The TOCs sure have more than a few similarities. Is this one a copycat?"Up: "No, it isn't. I see how this book is different, and see what it brings to the table. Oh, and first edition of this book came out in 2013, so this is actually the earliest book on the subject, it seems".In the end, a very positive evaluation. Yes, "High-performance Python" by by Gorelick and Ozsvald is an obvious competitor, and a strong one - a more advanced book than Lanaro's, I would guess. On the other hand, as a newbie to the world of high-performance Python, I know that I find Lanaro's explanation much easier to follow. This is the book I am actually going to use to learn the subject. Many thanks, Gabriele, five stars from me.

Amazon Verified review

Parijat Sengupta May 19, 2023

A neat compilation of online guides/tutorials. Convoluted explanations that are typical of short articles posted on multiple forums. The worst chapter is perhaps the one on implementing concurrency (chapter 6). A redeeming feature is a good explanatory section on the indexing patterns of Numpy. There isn’t much to expect from Packt books in general, as they seem to be put together in haste with quality control an obvious casualty - this title lives up to that billing.

Python High Performance, Second Edition: Build high-performing, concurrent, and distributed applications , Second Edition

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