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Total Size:
34.1 MB
Info Hash:
EF0066F13FBBC402112BC6549731538CEC29B66A
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April 20, 2026, 5:43 a.m.
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(Last updated: April 20, 2026, 5:48 a.m.)
| File | Size |
|---|---|
| Gorelick M. High Performance Python. Practical Performant Programming...2ed 2020.pdf | 10.6 MB |
| Gorelick M. High Performance Python. Practical Performant Programming...3ed 2025.pdf | 11.1 MB |
| Code.zip | 12.5 MB |
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| Uploaded by freecoursewb | Size 414.3 MB | Health [ 23 /12 ] | Added 2023-10-26 |
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SOURCE: Gorelick M. High Performance Python. Practical Performant Programming...3ed 2025
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Textbook in PDF format Your Python code may run correctly, but what if you need it to run faster? This practical book shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By explaining the fundamental theory behind design choices, this expanded edition of High Performance Python helps experienced Python programmers gain a deeper understanding of Python's implementation. How do you take advantage of multicore architectures or compilation? Or build a system that scales up beyond RAM limits or with a GPU? Authors Micha Gorelick and Ian Ozsvald reveal concrete solutions to many issues and include war stories from companies that use high-performance Python for GenAI data extraction, productionized Machine Learning, and more. 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 Process DataFrames quickly with Pandas, Dask, and Polars Speed up your neural networks and GPU computations Use tools to compile Python down to machine code Manage multiple I/O and computational operations concurrently Convert multiprocessing code to run on local or remote clusters Python is easy to learn. You’re probably here because now that your code runs correctly, you need it to run faster. You like the fact that your code is easy to modify and you can iterate with ideas quickly. The trade-off between easy to develop and runs as quickly as I need is a well-understood and often-bemoaned phenomenon. There are solutions. Some people have serial processes that have to run faster. Others have problems that could take advantage of multicore architectures, clusters, or graphics processing units. Some need scalable systems that can process more or less as expediency and funds allow, without losing reliability. Others will realize that their coding techniques, often borrowed from other languages, perhaps aren’t as natural as examples they see from others. In this book we will cover all of these topics, giving practical guidance for understanding bottlenecks and producing faster and more scalable solutions. We also include some war stories from those who went ahead of you, who took the knocks so you don’t have to. Python is well suited for rapid development, production deployments, and scalable systems. The ecosystem is full of people who are working to make it scale on your behalf, leaving you more time to focus on the more challenging tasks around you. Preface Understanding Performant Python Profiling to Find Bottlenecks Lists and Tuples Dictionaries and Sets Iterators and Generators Matrix and Vector Computation Pandas, Dask, and Polars Compiling to C Asynchronous I/O The multiprocessing Module Clusters and Job Queues Using Less RAM Lessons from the Field
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