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Diffstat (limited to 'doc/neps')
| -rw-r--r-- | doc/neps/missing-data.rst | 32 |
1 files changed, 32 insertions, 0 deletions
diff --git a/doc/neps/missing-data.rst b/doc/neps/missing-data.rst index 038ea9370..e83bd2189 100644 --- a/doc/neps/missing-data.rst +++ b/doc/neps/missing-data.rst @@ -743,6 +743,38 @@ to be consistent with the result of np.sum([]):: >>> np.sum([]) 0.0 +Boolean Indexing +================ + +Indexing using a boolean array containing NAs does not have a consistent +interpretation according to the NA abstraction. For example:: + + >>> a = np.array([1, 2]) + >>> mask = np.array([np.NA, True], maskna=True) + >>> a[mask] + What should happen here? + +Since the NA represents a valid but unknown value, and it is a boolean, +it has two possible underlying values:: + + >>> a[np.array([True, True])] + array([1, 2]) + >>> a[np.array([False, True])] + array([2]) + +The thing which changes is the length of the output array, nothing which +itself can be substituted for NA. For this reason, at least initially, +NumPy will raise an exception for this case. + +Another possibility is to add an inconsistency, and follow the approach +R uses. That is, to produce the following:: + + >>> a[mask] + array([NA, 2], maskna=True) + +If, in user testing, this is found necessary for pragmatic reasons, +the feature should be added even though it is inconsistent. + PEP 3118 ======== |