On Mon, Feb 14, 2022, at 12:45, Sebastian Berg wrote: > But this is currently *not* consistently the case. I wish to make this > consistent. The confusion is around object arrays, though: > > value = np.array(None, dtype=object) > arr[0] = value > > Stores `value` without unpacking it currently. > > arr.fill(value) > > Stores the `None` (unpacking `value`) if and only if `value` is 0-D.
That last behavior doesn't look right to me. An object array should be thought of as a collection of pointers, and if you happen to want to point to a NumPy array, so be it. > Further related behaviour is that: > > np.array(value) # unpacks any array > np.array([value, None], dtype=object) # does not unpack This seems reasonable. What would another reasonable expectation be? > Now, we could unpack making it impossible to place a 0-D array into an > object array except via `arr.itemset()`. Not sure why we'd do that. > My current preference is to store the 0-D arrays as-is, and basically > say that passing a 0-D array as value for: > > arr.fill(0d_arr) > arr[0] = 0d_arr # this is fine: arr[0, ...] = 0d_arr Does this differ from the current behavior? It looks to me like object arrays get correctly filled and assigned. Stéfan _______________________________________________ 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: arch...@mail-archive.com
