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| author | Ralf Gommers <ralf.gommers@googlemail.com> | 2012-03-25 14:44:23 +0200 |
|---|---|---|
| committer | Charles Harris <charlesr.harris@gmail.com> | 2012-03-30 05:41:00 -0600 |
| commit | 41f7d21182fddbc71b2100fdb3425a5e7e2f35b6 (patch) | |
| tree | 8832edb9727821436916611d456828a8c612a520 /doc | |
| parent | f84ac77e50456191a2574913494d1e5ce5a4d92b (diff) | |
| download | numpy-41f7d21182fddbc71b2100fdb3425a5e7e2f35b6.tar.gz | |
DOC: label the current NA implementation as experimental in 1.7.x
This has been extensively discussed on the mailing list. See #2072.
Diffstat (limited to 'doc')
| -rw-r--r-- | doc/release/2.0.0-notes.rst | 5 | ||||
| -rw-r--r-- | doc/source/reference/arrays.maskna.rst | 5 |
2 files changed, 10 insertions, 0 deletions
diff --git a/doc/release/2.0.0-notes.rst b/doc/release/2.0.0-notes.rst index 7c2bfa8a5..eec2b7933 100644 --- a/doc/release/2.0.0-notes.rst +++ b/doc/release/2.0.0-notes.rst @@ -18,6 +18,11 @@ Preliminary support for NA missing values similar to those in R has been implemented. This was done by adding optional NA masks to the core array object. +.. note:: The NA API is *experimental*, and may undergo changes in future + versions of NumPy. The current implementation based on masks will likely be + supplemented by a second one based on bit-patterns, and it is possible that + a difference will be made between missing and ignored data. + While a significant amount of the NumPy functionality has been extended to support NA masks, not everything is yet supported. Here is an (incomplete) list of things that do and do not work with NA values: diff --git a/doc/source/reference/arrays.maskna.rst b/doc/source/reference/arrays.maskna.rst index 2faabde83..bd9516eba 100644 --- a/doc/source/reference/arrays.maskna.rst +++ b/doc/source/reference/arrays.maskna.rst @@ -14,6 +14,11 @@ core ndarray. This system is highly flexible, allowing NAs to be used with any underlying dtype, and supports creating multiple views of the same data with different choices of NAs. +.. note:: The NA API is *experimental*, and may undergo changes in future + versions of NumPy. The current implementation based on masks will likely be + supplemented by a second one based on bit-patterns, and it is possible that + a difference will be made between missing and ignored data. + Other Missing Data Approaches ============================= |