[Numpy-discussion] missing data discussion round 2

[Mark Wiebe]

On Mon, Jun 27, 2011 at 12:44 PM, eat <e.antero.tammi at gmail.com> wrote:

> Hi,
>
> On Mon, Jun 27, 2011 at 6:55 PM, Mark Wiebe <mwwiebe at gmail.com> wrote:
>
>> First I'd like to thank everyone for all the feedback you're providing,
>> clearly this is an important topic to many people, and the discussion has
>> helped clarify the ideas for me. I've renamed and updated the NEP, then
>> placed it into the master NumPy repository so it has a more permanent home
>> here:
>>
>> https://github.com/numpy/numpy/blob/master/doc/neps/missing-data.rst
>>
>> In the NEP, I've tried to address everything that was raised in the
>> original thread and in Nathaniel's followup 'Concepts' thread. To deal with
>> the issue of whether a mask is True or False for a missing value, I've
>> removed the 'mask' attribute entirely, except for ufunc-like functions
>> np.ismissing and np.isavail which return the two styles of masks. Here's a
>> high level summary of how I'm thinking of the topic, and what I will
>> implement:
>>
>> *Missing Data Abstraction*
>>
>> There appear to be two useful ways to think about missing data that are
>> worth supporting.
>>
>> 1) Unknown yet existing data
>> 2) Data that doesn't exist
>>
>> In 1), an NA value causes outputs to become NA except in a small number of
>> exceptions such as boolean logic, and in 2), operations treat the data as if
>> there were a smaller array without the NA values.
>>
>> *Temporarily Ignoring Data*
>> *
>> *
>> In some cases, it is useful to flag data as NA temporarily, possibly in
>> several different ways, for particular calculations or testing out different
>> ways of throwing away outliers. This is independent of the missing data
>> abstraction, still requiring a choice of 1) or 2) above.
>>
>> *Implementation Techniques*
>> *
>> *
>> There are two mechanisms generally used to implement missing data
>> abstractions,
>> *
>> *
>> 1) An NA bit pattern
>> 2) A mask
>>
>> I've described a design in the NEP which can include both techniques using
>> the same interface. The mask approach is strictly more general than the NA
>> bit pattern approach, except for a few things like the idea of supporting
>> the dtype 'NA[f8,InfNan]' which you can read about in the NEP.
>>
>> My intention is to implement the mask-based design, and possibly also
>> implement the NA bit pattern design, but if anything gets cut it will be the
>> NA bit patterns.
>>
>> Thanks again for all your input so far, and thanks in advance for your
>> suggestions for improving this new revision of the NEP.
>>
> A very impressive PEP indeed.
>
> However, how would corner cases, like
>
> >>> a = np.array([np.NA, np.NA], dtype='f8', masked=True)
> >>> np.mean(a, skipna=True)
>
> This should be equivalent to removing all the NA values, then calling mean,
like this:

>>> b = np.array([], dtype='f8')
>>> np.mean(b)
/home/mwiebe/virtualenvs/dev/lib/python2.7/site-packages/numpy/core/fromnumeric.py:2374:
RuntimeWarning: invalid value encountered in double_scalars
  return mean(axis, dtype, out)
nan

>>> np.mean(a)
>
> This would return NA, since NA values are sitting in positions that would
affect the output result.


> be handled?
>
> My concern here is that there always seems to be such corner cases which
> can only be handled with specific context knowledge. Thus producing 100%
> generic code to handle 'missing data' is not doable.
>

Working out the corner cases for the functions that are already in numpy
seems tractable to me, how to or whether to support missing data is
something the author of each new function will have to consider when missing
data support is in NumPy, but I don't think we can do more than provide the
mechanisms for people to use.

-Mark


> Thanks,
> - eat
>
>>
>> -Mark
>>
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>>
>
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