Hi all, In https://github.com/numpy/numpy/pull/11897 I am looking into the addition of a `copy=np.never_copy` argument to: * np.array * arr.reshape/np.reshape * arr.astype Which would cause an error to be raised when numpy cannot guarantee that the returned array is a view of the input array. The motivation is to easier avoid accidental copies of large data, or ensure that in-place manipulation will be meaningful. The copy flag API would be: * `copy=True` forces a copy * `copy=False` allows numpy to copy if necessary * `copy=np.never_copy` will error if a copy would be necessary * (almost) all other input will be deprecated. Unfortunately using `copy="never"` is tricky, because currently `np.array(..., copy="never")` behaves exactly the same as `np.array(..., copy=bool("never"))`. So that the wrong result would be given on old numpy versions and it would be a behaviour change. Some things that are a not so nice maybe: * adding/using `np.never_copy` is not very nice * Scalars need a copy and so will not be allowed * For rare array-likes numpy may not be able to guarantee no-copy, although it could happen (but should not). The history is that a long while ago I considered adding a copy flag to `reshape` so that it is possible to do `copy=np.never_copy` (or similar) to ensure that no copy is made. In these, you may want something like an assertion: ``` new_arr = arr.reshape(new_shape) assert np.may_share_memory(arr, new_arr) # Which is sometimes -- but should not be -- written as: arr.shape = new_shape # unnecessary container modification # Or: view = np.array(arr, order="F") assert np.may_share_memory(arr, new_arr) ``` but is more readable and will not cause an intermediate copy on error. So what do you think? Other variants would be to not expose this for `np.array` and probably limit `copy="never"` to the reshape method. Or just to not do it at all. Or to also accept "never" for `reshape`, although I think I would prefer to keep it in sync and wait for a few years to consider that. Best, Sebastian