High Performance Python
Practical Performant Programming for Humans
Publisher: O'Reilly Media
Final Release Date: August 2014
Pages: 370

Your Python code may run correctly, but you need it to run faster. By exploring the fundamental theory behind design choices, this practical guide helps you gain a deeper understanding of Python’s implementation. You’ll learn how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs.

How can you take advantage of multi-core architectures or clusters? Or build a system that can scale up and down without losing reliability? Experienced Python programmers will learn concrete solutions to these and other issues, along with war stories from companies that use high performance Python for social media analytics, productionized machine learning, and other situations.

  • Get a better grasp of numpy, Cython, and profilers
  • Learn how Python abstracts the underlying computer architecture
  • Use profiling to find bottlenecks in CPU time and memory usage
  • Write efficient programs by choosing appropriate data structures
  • Speed up matrix and vector computations
  • Use tools to compile Python down to machine code
  • Manage multiple I/O and computational operations concurrently
  • Convert multiprocessing code to run on a local or remote cluster
  • Solve large problems while using less RAM
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oreillyHigh Performance Python
 
4.8

(based on 4 reviews)

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100%

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Pros

  • Helpful examples (4)
  • Easy to understand (3)
  • Well-written (3)

Cons

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  • Intermediate (4)
  • Expert (3)
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5.0

Dive into approaches and tools to hone

By Scott H.

from NY, NY

About Me Developer

Verified Buyer

Pros

  • Helpful examples
  • Well-written

Cons

    Best Uses

    • Expert
    • Intermediate

    Comments about oreilly High Performance Python:

    I've learned a lot about Python internals and tools to profile code. It strikes a good balance between assuming an intermediate background in Python but not necessarily a hard-core computer science background. Great for learning approaches for profiling and optimizing your code, as well as parallelizing it.

     
    5.0

    Comprehensive and very good book

    By Miske

    from Boston, MA

    About Me Developer

    Pros

    • Easy to understand
    • Helpful examples
    • Well-written

    Cons

      Best Uses

      • Intermediate

      Comments about oreilly High Performance Python:

      This book covers most of scenarios in which user wants to speed up code. It is fairly comprehensive in its scope. Text is easy to follow and working examples emphasize underlying techniques, assuming user has intermediate knowledge of Python. Book can be used as quick, one stop reference or guide for high performance Python.

       
      5.0

      Great summary of all the latest tech

      By Volodymyr

      from London, UK

      About Me Developer, Manager

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      Pros

      • Accurate
      • Concise
      • Easy to understand
      • Helpful examples
      • Well-written

      Cons

      • Will Get Outdated Quickly

      Best Uses

      • Expert
      • Intermediate

      Comments about oreilly High Performance Python:

      I needed to speed-up few simulations in python and this book saved me lot's of googling/stackoverflow searching time. It does a great job summarising everything that is out there (late 2014), so you know when to use numpy vs. cython vs. parallel computing. Lot's of good examples and nicely written.

      (1 of 3 customers found this review helpful)

       
      4.0

      A must read for python professionals!

      By nxvl

      from Lima, Peru

      About Me Developer, Maker, Sys Admin

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      Pros

      • Accurate
      • Easy to understand
      • Helpful examples

      Cons

        Best Uses

        • Expert
        • Intermediate

        Comments about oreilly High Performance Python:

        I hope this book was written 4 years ago! It provides a lot of inside for the problems we get into when working with Big Data and heavy process automation. While it doesn't cover some other important issues and problems, like DBs, it provides a framework to translate the tips and tricks presented on the book to that particular area. Can't wait to read the final product!

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