[Numpy-discussion] missing data discussion round 2
Mon Jun 27 10:55:45 CDT 2011
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 implement:
*Missing Data Abstraction*
There appear to be two useful ways to think about missing data that are
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.
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.
Thanks again for all your input so far, and thanks in advance for your
suggestions for improving this new revision of the NEP.
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