ah yes, that's also an issue I was trying to deal with. the semantics I prefer in these type of operators, is (as a default), to have every array be treated as a sequence of keys, so if calling unique(arr_2d), youd get unique rows, unless you pass axis=None, in which case the array is flattened.

I also agree that the extension you propose here is useful; but ideally, with a little more discussion on these subjects we can converge on an even more comprehensive overhaul

On Tue, Aug 12, 2014 at 6:33 PM, Joe Kington <joferkington@gmail.com> wrote:

On Tue, Aug 12, 2014 at 11:17 AM, Eelco Hoogendoorn <hoogendoorn.eelco@gmail.com> wrote:

Thanks. Prompted by that stackoverflow question, and similar problems I had to deal with myself, I started working on a much more general extension to numpy's functionality in this space. Like you noted, things get a little panda-y, but I think there is a lot of panda's functionality that could or should be part of the numpy core, a robust set of grouping operations in particular.

see pastebin here:
http://pastebin.com/c5WLWPbp

On a side note, this is related to a pull request of mine from awhile back: https://github.com/numpy/numpy/pull/3584

There was a lot of disagreement on the mailing list about what to call a "unique slices along a given axis" function, so I wound up closing the pull request pending more discussion. 

At any rate, I think it's a useful thing to have in "base" numpy.

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