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High Performance Python - Micha Gorelick PDF

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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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