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| -rw-r--r-- | doc/neps/missing-data.rst | 27 |
1 files changed, 27 insertions, 0 deletions
diff --git a/doc/neps/missing-data.rst b/doc/neps/missing-data.rst index 1fdd01ed9..52456ac47 100644 --- a/doc/neps/missing-data.rst +++ b/doc/neps/missing-data.rst @@ -591,6 +591,33 @@ cannot hold values, but will conform to the input types in functions like maps to [('a', 'NA[f4]'), ('b', 'NA[i4]')]. Thus, to view the memory of an 'f8' array 'arr' with 'NA[f8]', you can say arr.view(dtype='NA'). +Future Expansion to multi-NA Payloads +===================================== + +The packages SAS and Stata both support multiple different "NA" values. +This allows one to specify different reasons for why a value, for +example homework that wasn't done because the dog ate it or the student +was sick. In these packages, the different NA values have a linear ordering +which specifies how different NA values combine together. + +In the sections on C implementation details, the mask has been designed +so that a mask with a payload is a strict superset of the NumPy boolean +type, and the boolean type has a payload of just zero. Different payloads +combine with the 'min' operation. + +The important part of future-proofing the design is making sure +the C ABI-level choices and the Python API-level choices have a natural +transition to multi-NA support. Here is one way multi-NA support could look:: + + >>> a = np.array([np.NA(1), 3, np.NA(2)], namasked='multi') + >>> np.sum(a) + NA(1) + >>> np.sum(a[1:]) + NA(2) + >>> b = np.array([np.NA, 2, 5], namasked=True) + >>> a + b + array([NA(0), 5, NA(2)], namasked='multi') + PEP 3118 ======== |