[Numpy-discussion] missing data discussion round 2
Dag Sverre Seljebotn
Wed Jun 29 09:35:14 CDT 2011
On 06/29/2011 03:45 PM, Matthew Brett wrote:
> On Wed, Jun 29, 2011 at 12:39 AM, Mark Wiebe<email@example.com> wrote:
>> On Tue, Jun 28, 2011 at 5:20 PM, Matthew Brett<firstname.lastname@example.org>
>>> On Tue, Jun 28, 2011 at 4:06 PM, Nathaniel Smith<email@example.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 = np.NA', what you mean is,
>>> 'a._mask = np.ones(a.shape, np.dtype(bool); a._mask = 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 = np.NA
> "Oh, so you just set the a value to have some missing value code?"
> "Ah - no - in fact what I did was set a associated mask in position
> a so that you can't any longer see the previous value of a"
> "Huh. You mean I have a mask for every single value in order to be
> able to blank out a? It looks like an assignment. I mean, it
> looks just like a = 4. But I guess it isn't?"
> 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
> 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 = True. "Explicit is better than implicit".
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