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Comment by bloblaw | Hacker News Reader

The number of them isn't as relevant as their usage. Lots of data science related modules are effectively wrappers around C code. Here's a few:

  NumPy 
  pandas 
  Matplotlib 
  TensorFlow 
  PyTorch

These modules are critical to many of the workloads using Python (ML / AI / Data Science)

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Right this is what I thought - a small number of popular ones, rather than an overall majority across pypi packages

In the data science space, which is probably one of the biggest users of Python, an interpreter that doesn't support these is dead in the water.

I think the question is whether it would suffice to port the few most popular native modules to directly use this project's bespoke Rust API instead of the standard CPython API. This'd probably be less work than building a real emulation layer for the CPython API, especially if they don't want to include the GIL. But if Python users need a lot of different native modules and not just a few popular ones, then that won't suffice.

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