Add axis argument to numpy.unique
#7742
Conversation
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Hi Martino, thanks for reviving this. I seem to recall from the original discussion that we weren't able to reach an agreement on what If you could go ahead and bring the discussion up on the mailing list again, that'd be great... We really need to get some consensus on this beforewe can merge your code, which other than that seems mostly ready to go. |
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RE unique rows or unique columns For me, it's |
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The current behaviour is: >>> a = array([[0, 0, 0],
[1, 1, 1],
[0, 0, 0]])
>>> unique(a, axis=0)
array([[0, 0, 0],
[1, 1, 1]])So |
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@martinosorb - Glad to see you revived this! Things have been a bit hectic for me lately, so I never got around to adding the |
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Hi @joferkington, I tried to attract your attention at your old pull request, but I couldn't contact you. I hope you don't mind me using your code. |
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@martinosorb - I don't mind at all! I actually did get your ping on the old PR, but it got lost amongst everything else in my inbox, and I forgot to reply. My apologies for that. |
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There was only one reply on the mailing list. How shall we take this forward? |
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The code look OK at first sight, but I'll do a proper code review during this week. |
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This appears to solve a problem I posted earlier this year (#7273). Any more review necessary on the patch? |
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We were waiting for opinions on the mailing list, there was only one, which agreed with the current solution. I don't know about anything else. |
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Even though we didn't get a lot of replies on the mailing list, I think this is a simple and useful enough extension of current functionality that this is OK from the perspective of expanding the API. |
| if abs(axis) > ar.ndim: | ||
| raise ValueError('Invalid axis kwarg specified for unique') | ||
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| ar = np.swapaxes(ar, axis, 0) |
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Use np.moveaxis(ar, axis, 0) instead, which avoids the need to swap axes back at the end.
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Sorry, I may be missing something. Why so? That axis doesn't disappear.
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Not sure if you guys get notified of comments under comments, so writing here. Thoughts? |
| @@ -190,7 +192,7 @@ def unique(ar, return_index=False, return_inverse=False, | |||
| ar = np.asanyarray(ar) | |||
| if axis is None: | |||
| return _unique1d(ar, return_index, return_inverse, return_counts) | |||
| if abs(axis) > ar.ndim: | |||
| if abs(axis) > ar.ndim or axis == ar.ndim: | |||
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I would suggest (basically one less case to think about):
if not (-ar.ndim <= axis < ar.ndim):
@martinosorb I do get notified of comments... but I lost track of this one. Thanks for the reminder! |
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Done as you said, thanks for the comments. Let's see what CI says. |
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@charris I think this is basically ready to merge (after squashing and adding release notes). Am I correct that this is too late for 1.12 and should be targeted for 1.13 instead? |
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Yes, the line needs to be drawn somewhere. If you feel that it is really important I can sneak it in, but otherwise I'd rather move on with 1.13. Small bug fixes will still go into 1.12. |
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@charris Thanks, that's what I thought. @martinosorb Could you please rebase this to a single commit and add something about this to the 1.13 release notes? |
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Should be OK now. |
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Thanks @martinosorb ! |
| try: | ||
| consolidated = ar.view(dtype) | ||
| except TypeError: | ||
| # There's no good way to do this for object arrays, etc... |
This is an attempt at reviving joferkington's pull request (#3584, I was unable to contact him and his fork is no longer available). It makes use mostly of his code, but updates it to supporting return_counts, which was added later.
The idea is being able to return the unique rows (or columns) of a 2d array, or the unique (N-1)-dimensional slices of a N-dimensional array. If an N-dimensional array is provided, the function will return an N-dimensional array where the duplicate (N-1)-dimensional arrays found along the provided axis were removed.
Future work could involve providing more than an axis at once, for example when one wants to find the unique rows of a 10 dimensional array.