[Numpy-discussion] missing data discussion round 2

Pierre GM pgmdevlist@gmail....
Wed Jun 29 12:05:22 CDT 2011

Matthew, Dag, +1.
On Jun 29, 2011 4:35 PM, "Dag Sverre Seljebotn" <d.s.seljebotn@astro.uio.no>
> On 06/29/2011 03:45 PM, Matthew Brett wrote:
>> Hi,
>> On Wed, Jun 29, 2011 at 12:39 AM, Mark Wiebe<mwwiebe@gmail.com> wrote:
>>> On Tue, Jun 28, 2011 at 5:20 PM, Matthew Brett<matthew.brett@gmail.com>
>>> wrote:
>>>> Hi,
>>>> On Tue, Jun 28, 2011 at 4:06 PM, Nathaniel Smith<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
>>> on paper, then have several cardboards with holes poked in them in
>>> configurations. Placing these cardboard masks in front of the grid would
>>> show different sets of non-missing data, without affecting the values
>>> 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
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