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A Major Limitation of NumPy Which Most Users Aren't Aware Of

..and here's how to address it.

Jun 17, 2023

NumPy undoubtedly offers

extremely fast, and
optimized operations.

Yet, it DOES NOT support parallelism.

This provides further scope for run-time improvement.

Numexpr is a fast evaluator for NumPy expression, which uses:

multi-threading
just-in-time compilation

The speedup offered by Numexpr is evident from the image above.

Depending upon the complexity of the expression, the speed-ups can range from 0.95x and 20x.

Read more: Documentation.

👉 Over to you: What are some other ways to speedup NumPy computation?

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Find the code for my tips here: GitHub.

I like to explore, experiment and write about data science concepts and tools. You can read my articles on Medium. Also, you can connect with me on LinkedIn and Twitter.