On Fri, 2013-04-19 at 08:03 -0700, Chris Barker - NOAA Federal wrote: > On Apr 18, 2013, at 11:33 PM, Nathaniel Smith <n...@pobox.com> wrote: > > > > > On 18 Apr 2013 01:29, "Chris Barker - NOAA Federal" > > <chris.bar...@noaa.gov> wrote: > > > This has been annoying, particular as rank-zero scalars are kind > > of a pain. > > > > BTW, while we're on the topic, can you elaborate on this? I tend to > > think scalars (as opposed to 0d ndarrays) are kind of a pain, so I'm > > curious if you have specific issues you've run into with 0d > > ndarrays. > > > > > Well, I suppose what's really a pain is that we have both, and they > are not the same, and neither can be used in all cases one may want. > > > In the case at hand, I really wanted a datetime64 scalar. By saving > and re-loading in an npz, it got converted to a rank-zero array, which > had different behavior. In this case, the frustrating bit was how to > extract a scalar again ( which I really wanted to turn into a datetime > object). > > > After the fact, I discovered .item(), which seems to do what I want. > Fun fact, array[()] will convert a 0-d array to a scalar, but do nothing (or currently create a view) for other arrays. Which is actually a good question. Should array[()] force a view or not?
- Sebastian > > On a phone now, so sorry about the lack of examples. > > > Note: I've lost track of why we need both scalers and rank-zero > arrays. I can't help thinking that there could be an object that acts > like a scalar in most contexts, but also has the array methods that > make sense. > > > But I know it's far from simple. > > > -Chris > > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
