On 06/29/2011 03:45 PM, Matthew Brett wrote: > Hi, > > On Wed, Jun 29, 2011 at 12:39 AM, Mark Wiebe<mwwi...@gmail.com> wrote: >> On Tue, Jun 28, 2011 at 5:20 PM, Matthew Brett<matthew.br...@gmail.com> >> wrote: >>> >>> Hi, >>> >>> On Tue, Jun 28, 2011 at 4:06 PM, Nathaniel Smith<n...@pobox.com> wrote: >>> ... >>>> (You might think, what difference does it make if you *can* unmask an >>>> item? Us missing data folks could just ignore this feature. But: >>>> whatever we end up implementing is something that I will have to >>>> explain over and over to different people, most of them not >>>> particularly sophisticated programmers. And there's just no sensible >>>> way to explain this idea that if you store some particular value, then >>>> it replaces the old value, but if you store NA, then the old value is >>>> still there. >>> >>> Ouch - yes. No question, that is difficult to explain. Well, I >>> think the explanation might go like this: >>> >>> "Ah, yes, well, that's because in fact numpy records missing values by >>> using a 'mask'. So when you say `a[3] = np.NA', what you mean is, >>> 'a._mask = np.ones(a.shape, np.dtype(bool); a._mask[3] = False`" >>> >>> Is that fair? >> >> My favorite way of explaining it would be to have a grid of numbers written >> on paper, then have several cardboards with holes poked in them in different >> configurations. Placing these cardboard masks in front of the grid would >> show different sets of non-missing data, without affecting the values stored >> on the paper behind them. > > Right - but here of course you are trying to explain the mask, and > this is Nathaniel's point, that in order to explain NAs, you have to > explain masks, and so, even at a basic level, the fusion of the two > ideas is obvious, and already confusing. I mean this: > > a[3] = np.NA > > "Oh, so you just set the a[3] value to have some missing value code?" > > "Ah - no - in fact what I did was set a associated mask in position > a[3] so that you can't any longer see the previous value of a[3]" > > "Huh. You mean I have a mask for every single value in order to be > able to blank out a[3]? It looks like an assignment. I mean, it > looks just like a[3] = 4. But I guess it isn't?" > > "Er..." > > I think Nathaniel's point is a very good one - these are separate > ideas, np.NA and np.IGNORE, and a joint implementation is bound to > draw them together in the mind of the user. Apart from anything > else, the user has to know that, if they want a single NA value in an > array, they have to add a mask size array.shape in bytes. They have > to know then, that NA is implemented by masking, and then the 'NA for > free by adding masking' idea breaks down and starts to feel like a > kludge. > > The counter argument is of course that, in time, the implementation of > NA with masking will seem as obvious and intuitive, as, say, > broadcasting, and that we are just reacting from lack of experience > with the new API.
However, no matter how used we get to this, people coming from almost any other tool (in particular R) will keep think it is counter-intuitive. Why set up a major semantic incompatability that people then have to overcome in order to start using NumPy. I really don't see what's wrong with some more explicit API like a.mask[3] = True. "Explicit is better than implicit". Dag Sverre _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
