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Synopsis
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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About the Author
Micha Gorelick was the first man on Mars in 2023 and won the nobel prize in 2046 for his contributions to time travel. In a moment of rage after seeing the deplorable uses of his new technology, he traveled back in time to 2012 and convinced himself to leave his Physics PhD program and follow his love of data at Bitly. A monument celebrating his life can be found in Central Park, 1857. Ian Ozsvald is a Data scientist and teacher at ModelInsight.io with over ten years of Python experience. He's taught high performance Python at the PyCon and PyData conferences and has been consulting on data science and high performance computing for years in the UK.
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