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Python High Performance, Second Edition
Python High Performance, Second Edition

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

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Profile Icon Dr. Gabriele Lanaro
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Can$39.99 Can$44.99
Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (2 Ratings)
eBook May 2017 270 pages 2nd Edition
eBook
Can$39.99 Can$44.99
Paperback
Can$55.99
Subscription
Free Trial
Arrow left icon
Profile Icon Dr. Gabriele Lanaro
Arrow right icon
Can$39.99 Can$44.99
Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (2 Ratings)
eBook May 2017 270 pages 2nd Edition
eBook
Can$39.99 Can$44.99
Paperback
Can$55.99
Subscription
Free Trial
eBook
Can$39.99 Can$44.99
Paperback
Can$55.99
Subscription
Free Trial

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Python High Performance, Second Edition

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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.

Who is this book for?

The book is aimed at Python developers who want to improve the performance of their application. Basic knowledge of Python is expected

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : May 24, 2017
Length: 270 pages
Edition : 2nd
Language : English
ISBN-13 : 9781787282438
Category :
Languages :

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
OR
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Payment Processing...
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Product Details

Publication date : May 24, 2017
Length: 270 pages
Edition : 2nd
Language : English
ISBN-13 : 9781787282438
Category :
Languages :

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Frequently bought together


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Total Can$ 173.97
Python High Performance, Second Edition
Can$55.99
Python Data Structures and Algorithms
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Daniel Arbuckle???s Mastering Python
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Total Can$ 173.97 Stars icon

Table of Contents

9 Chapters
Benchmarking and Profiling Chevron down icon Chevron up icon
Pure Python Optimizations Chevron down icon Chevron up icon
Fast Array Operations with NumPy and Pandas Chevron down icon Chevron up icon
C Performance with Cython Chevron down icon Chevron up icon
Exploring Compilers Chevron down icon Chevron up icon
Implementing Concurrency Chevron down icon Chevron up icon
Parallel Processing Chevron down icon Chevron up icon
Distributed Processing Chevron down icon Chevron up icon
Designing for High Performance Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
(2 Ratings)
5 star 50%
4 star 0%
3 star 50%
2 star 0%
1 star 0%
Dimitri Shvorob Feb 23, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Amazon
Parijat Sengupta May 19, 2023
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
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.
Amazon Verified review Amazon
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