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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 pand
by bloblaw 4y ago
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)
- smcl 4y agoRight this is what I thought - a small number of popular ones, rather than an overall majority across pypi packages
- plonk 4y agoIn 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.
- ameliaquining 4y agoI 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.