Hey Nathaniel, Fascinating! Thanks for the primer! I didn't know that it would check dtype of values in the whole array. In that case, I would agree that it would be bad to infer it magically from just the first value, and this can be left to the users. Thanks! Juan. On Sat, Dec 12, 2015 at 7:00 PM, Nathaniel Smith <njs@pobox.com> wrote:

On Fri, Dec 11, 2015 at 11:32 PM, Juan Nunez-Iglesias <jni.soma@gmail.com> wrote:

Nathaniel,

IMO this is better than making np.array(iter) internally call list(iter) or equivalent

Yeah but that's not the only option:

from itertools import chain def fromiter_awesome_edition(iterable): elem = next(iterable) dtype = whatever_numpy_does_to_infer_dtypes_from_lists(elem) return np.fromiter(chain([elem], iterable), dtype=dtype)

I think this would be a huge win for usability. Always getting tripped up by the dtype requirement. I can submit a PR if people like this pattern.

This isn't the semantics of np.array, though -- np.array will look at the whole input and try to find a common dtype, so this can't be the implementation for np.array(iter). E.g. try np.array([1, 1.0])

I can see an argument for making the dtype= argument to fromiter optional, with a warning in the docs that it will guess based on the first element and that you should specify it if you don't want that. It seems potentially a bit error prone (in the sense that it might make it easier to end up with code that works great when you test it but then breaks later when something unexpected happens), but maybe the usability outweighs that. I don't use fromiter myself so I don't have a strong opinion.

btw, I think np.array(['f', 'o', 'o']) would be exactly the expected result for np.array('foo'), but I guess that's just me.

In general np.array(thing_that_can_go_inside_an_array) returns a zero-dimensional (scalar) array -- np.array(1), np.array(True), etc. all work like this, so I'd expect np.array("foo") to do the same.

-n

-- Nathaniel J. Smith -- http://vorpus.org _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion