> <mailto:
njs@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'm not aware of a semantic incompatibility. I believe R doesn't support
> views like NumPy does, so the things you have to do to see masking
> semantics aren't even possible in R.