[ameliaquining]

What kind of subset would you have in mind? I think that any kind of numeric operation would be off the table, for the reasons given in PEP 465:

"Providing a quality implementation of matrix multiplication is highly non-trivial. Naive nested loop implementations are very slow and shipping such an implementation in CPython would just create a trap for users. But the alternative – providing a modern, competitive matrix multiply – would require that CPython link to a BLAS library, which brings a set of new complications. In particular, several popular BLAS libraries (including the one that ships by default on OS X) currently break the use of multiprocessing."

[im3w1l]

Numpy is incredibly widespread and basically a standard so I would propose: It should have exactly the same layout in memory as a numpy array. It's fine if it has a very limited set of operations out-of-the-box. Maybe something like get, set, elementwise-arithmetic. Work with numpy project to make it possible to cast it to numpy array to help the common case where someone is fine with a dep on numpy and wants the full set of numpy operations.

[coldtea]

The best they can do without BLAS. Doesn't have to be as fast as numpy, just faster and more memory efficient than doing it in native Python, without the dependency.