BUG: Fix np.unique with axis=0 and 1D input not collapsing NaNs with equal_nan=True #29336 #29372
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AnkitAhlawat7742
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Hi @ngoldbaum , |
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Haven't looked at this, but one note/question is how it ties back to: #27345 (and maybe there is even something relevant for this there). |
@seberg , |
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@seberg are we going to merge this PR ? ,as it is a Quick fix for 1st case(For 1D arrays, np.nan values are not treated as equal even when equal_nan=True.) |
@seberg Thanks for the feedback on the doc changes — let’s continue the discussion in a second PR as you suggested! |
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Nothing to do, thanks for the PR and nice tests! Just waiting for CI to merge. |
It's passing now! I'd hit the button but I don't quite have time to read through this whole thread again... |
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What was the Issue
Issue link :- #29336
Bug Description:- The equal_nan parameter in numpy.unique does not work correctly in some cases. Specifically:
For 1D arrays, np.nan values are not treated as equal even when equal_nan=True.
For 2D arrays with axis=0, the issue is similar — np.nan values are not treated as equal.
What This PR Fixes
This pull request fixes the issue for 1D arrays only.
Fixed: np.unique(..., equal_nan=True) now treats NaNs as equal in 1D arrays.
Notes
This PR does not address the 2D case (e.g., np.unique(array, axis=0, equal_nan=True)) described in the same issue.In coming days i will investigate and provide fix for this case as well