[Numpy-discussion] feedback request: proposal to add masks to the core ndarray
Wes McKinney
wesmckinn@gmail....
Sat Jun 25 09:14:47 CDT 2011
On Sat, Jun 25, 2011 at 12:42 AM, Charles R Harris
<charlesr.harris@gmail.com> wrote:
>
>
> On Fri, Jun 24, 2011 at 10:06 PM, Wes McKinney <wesmckinn@gmail.com> wrote:
>>
>> On Fri, Jun 24, 2011 at 11:59 PM, Nathaniel Smith <njs@pobox.com> wrote:
>> > On Fri, Jun 24, 2011 at 6:57 PM, Benjamin Root <ben.root@ou.edu> wrote:
>> >> On Fri, Jun 24, 2011 at 8:11 PM, Nathaniel Smith <njs@pobox.com> wrote:
>> >>> This is a situation where I would just... use an array and a mask,
>> >>> rather than a masked array. Then lots of things -- changing fill
>> >>> values, temporarily masking/unmasking things, etc. -- come from free,
>> >>> just from knowing how arrays and boolean indexing work?
>> >>
>> >> With a masked array, it is "for free". Why re-invent the wheel? It
>> >> has
>> >> already been done for me.
>> >
>> > But it's not for free at all. It's an additional concept that has to
>> > be maintained, documented, and learned (with the last cost, which is
>> > multiplied by the number of users, being by far the greatest). It's
>> > not reinventing the wheel, it's saying hey, I have wheels and axles,
>> > but what I really need the library to provide is a wheel+axle
>> > assembly!
>>
>> You're communicating my argument better than I am.
>>
>> >>> Do we really get much advantage by building all these complex
>> >>> operations in? I worry that we're trying to anticipate and write code
>> >>> for every situation that users find themselves in, instead of just
>> >>> giving them some simple, orthogonal tools.
>> >>>
>> >>
>> >> This is the danger, and which is why I advocate retaining the
>> >> MaskedArray
>> >> type that would provide the high-level "intelligent" operations,
>> >> meanwhile
>> >> having in the core the basic data structures for pairing a mask with
>> >> an
>> >> array, and to recognize a special np.NA value that would act upon the
>> >> mask
>> >> rather than the underlying data. Users would get very basic
>> >> functionality,
>> >> while the MaskedArray would continue to provide the interface that we
>> >> are
>> >> used to.
>> >
>> > The interface as described is quite different... in particular, all
>> > aggregate operations would change their behavior.
>> >
>> >>> As a corollary, I worry that learning and keeping track of how masked
>> >>> arrays work is more hassle than just ignoring them and writing the
>> >>> necessary code by hand as needed. Certainly I can imagine that *if the
>> >>> mask is a property of the data* then it's useful to have tools to keep
>> >>> it aligned with the data through indexing and such. But some of these
>> >>> other things are quicker to reimplement than to look up the docs for,
>> >>> and the reimplementation is easier to read, at least for me...
>> >>
>> >> What you are advocating is similar to the "tried-n-true" coding
>> >> practice of
>> >> Matlab users of using NaNs. You will hear from Matlab programmers
>> >> about how
>> >> it is the greatest idea since sliced bread (and I was one of them).
>> >> Then I
>> >> was introduced to Numpy, and I while I do sometimes still do the NaN
>> >> approach, I realized that the masked array is a "better" way.
>> >
>> > Hey, no need to go around calling people Matlab programmers, you might
>> > hurt someone's feelings.
>> >
>> > But seriously, my argument is that every abstraction and new concept
>> > has a cost, and I'm dubious that the full masked array abstraction
>> > carries its weight and justifies this cost, because it's highly
>> > redundant with existing abstractions. That has nothing to do with how
>> > tried-and-true anything is.
>>
>> +1. I think I will personally only be happy if "masked array" can be
>> implemented while incurring near-zero cost from the end user
>> perspective. If what we end up with is a faster implementation of
>> numpy.ma in C I'm probably going to keep on using NaN... That's why
>> I'm entirely insistent that whatever design be dogfooded on non-expert
>> users. If it's very much harder / trickier / nuanced than R, you will
>> have failed.
>>
>
> This sounds unduly pessimistic to me. It's one thing to suggest different
> approaches, another to cry doom and threaten to go eat worms. And all before
> the code is written, benchmarks run, or trial made of the usefulness of the
> approach. Let us see how things look as they get worked out. Mark has a good
> track record for innovative tools and I'm rather curious myself to see what
> the result is.
>
> Chuck
>
>
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>
I hope you're right. So far it seems that anyone who has spent real
time with R (e.g. myself, Nathaniel) has expressed serious concerns
about the masked approach. And we got into this discussion at the Data
Array summit in Austin last month because we're trying to make Python
more competitive with R viz statistical and financial applications.
I'm just trying to be (R)ealistic =P Remember that I very earnestly am
doing everything I can these days to make scientific Python more
successful in finance and statistics. One big difference with R's
approach is that we care more about performance the the R community
does. So maybe having special NA values will be prohibitive for that
reason.
Mark indeed has a fantastic track record and I've been extremely
impressed with his NumPy work, so I've no doubt he'll do a good job. I
just hope that you don't push aside my input-- my opinions are formed
entirely based on my domain experience.
-W
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