Hi everyone, I've recently put together a pull request that adds an `axis` kwarg to `numpy.unique` so that `unique`can easily be used to find unique rows/columns/sub-arrays/etc of a larger array.
https://github.com/numpy/numpy/pull/3584 Currently, this works as a warpper around `unique`. If `axis` is specified, it reshapes the input to a 2D contiguous array, views each row as a single item, then passes it on to `unique`. For int and string dtypes, each row is viewed as a void dtype and therefore bitwise-equality is used for comparisons. For all other dtypes, the each row is viewed as a structured array. The current implementation has two main drawbacks: 1. For anything other than ints and strings, it's relatively slow. 2. It doesn't work with object arrays of any sort. I'd appreciate any thoughts/feedback folks might have on both the general idea and this specific implementation. It think it's a worthwhile addition, but I'm biased. Thanks! -Joe
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