[f33d5173]

array is for serializing to/from binary data. It isn't useful for returning from a library because the only way a python programmer can consume it is by converting into python objects, at which point there is no efficiency benefit. numpy has a library of functions for operating directly on the referenced data, as well as a cottage industry of libraries that will take a numpy array as input. Obviously someone might end up casting it to a list anyways, but there is at least the opportunity for them to not do that.

[sgarland]

multiprocessing.shared_memory.ShareableList can be useful in some circumstances, even if you don’t intend on sharing it across processes. It allows direct access to the data, elements are mutable (to an extent; you can’t increase the size of either the overall list or its elements once built), and since the underlying shm is exposed, you can get memoryviews for zero-copy.

The downside is they’re on the more esoteric side of Python, so people may not be as familiar with them as other structures.

[dumah]

Cython implements a C api for accessing the underlying data structure.

Arrays implement the buffer interface so they can be used efficiently with tools like numpy.