It is possible to do this using the new DType system. Sebastian wrote a sketch for a DType backed by the GNU multiprecision float library: https://github.com/numpy/numpy-user-dtypes/tree/main/mpfdtype
It adds a significant amount of complexity to store data outside the array buffer and introduces the possibility of use-after-free and dangling reference errors that are impossible if the array does not use embedded references, so that’s the main reason it hasn’t been done much. On Wed, Mar 13, 2024 at 8:17 AM Dom Grigonis <dom.grigo...@gmail.com> wrote: > Hi all, > > Say python’s builtin `int` type. It can be as large as memory allows. > > np.ndarray on the other hand is optimized for vectorization via strides, > memory structure and many things that I probably don’t know. Well the point > is that it is convenient and efficient to use for many things in comparison > to python’s built-in list of integers. > > So, I am thinking whether something in between exists? (And obviously > something more clever than np.array(dtype=object)) > > Probably something similar to `StringDType`, but for integers and floats. > (It’s just my guess. I don’t know anything about `StringDType`, but just > guessing it must be better than np.array(dtype=object) in combination > with np.vectorize) > > Regards, > dgpb > > _______________________________________________ > NumPy-Discussion mailing list -- numpy-discussion@python.org > To unsubscribe send an email to numpy-discussion-le...@python.org > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > Member address: nathan12...@gmail.com >
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