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

..and here's how to address it.

June 17, 2023 • Reading Time: 2 minutes

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.

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