[Numpy-discussion] missing data discussion round 2
Mon Jun 27 12:18:57 CDT 2011
On Mon, Jun 27, 2011 at 5:53 PM, Charles R Harris
> On Mon, Jun 27, 2011 at 9:55 AM, Mark Wiebe <firstname.lastname@example.org> 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
>> 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
>> 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
>> 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.
> I have the impression that the mask-based design would be easier. Perhaps
> you could do that one first and folks could try out the API and see how they
> like it and discover whether the memory overhead is a problem in practice.
That seems like a risky strategy to me, as the most likely outcome is
that people worried about memory will avoid masked arrays because they
know they use more memory. The memory usage is predictable and we
won't learn any more about it from use. We most of us already know if
we're having to optimize code for memory.
You won't get complaints, you'll just lose a group of users, who will,
I suspect, stick to NaNs, unsatisfactory as they are.
More information about the NumPy-Discussion