> there is no libnumpy or libpandas
I think this is changing with the rise of Apache Arrow. See eg here, slide 38 and on:
https://www.slideshare.net/wesm/pycon-colombia-2020-python-f...
I think this is changing with the rise of Apache Arrow. See eg here, slide 38 and on:
https://www.slideshare.net/wesm/pycon-colombia-2020-python-f...
Blackbear's comment was that writing libraries in C allows those libraries to be deployed broadly in many compute environments.
Jakob's reply (as I understood it) was that outside of the big Deep Learning libraries, this has not really happened. There is no C implementation of Pandas that allows for redeployment in other non-python compute contexts.
My point was that, with Arrow, this type of cross platform compatibility is coming to python dataframe libraries. You can prototype Dask code that runs on your laptop, then deploy it to a production Spark cluster, knowing the same Arrow engine is underpinning both. Or at least that's the vision. Obviously Arrow is still relatively young. But the point is, it's far from certain that the long-term global optimum for the ecosystem isn't sticking with "all libraries are written in C".
> it didn't even provide a shape attribute
I suspect this has to do with the project's focus. I think they aspire to be a back-end to DataFrame libraries, which are generally 2d. I think they (correctly) are ceding the "n-dimensional tensor computation" space to the current incumbents.
https://www.slideshare.net/wesm/pycon-colombia-2020-python-f...
Slide 43: The "Arrow C++ Platform" encompasses a "Multi-core Work Scheduler" and a "Query Engine"
Slide 38: "It would be more productive (long-term) to have a reusable computational foundation for data frames"
Again, I agree that, today, it's more data format, and the shared compute stuff is more a vision.
EDIT: See also https://ursalabs.org/tech/