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Alex Rivera
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I'm using a 2D shape array to store pairs of longitudes+latitudes. At one point, I have to merge two of these 2D arrays, and then remove any duplicated entry. I've been searching for a function similar to numpy.unique, but I've had no luck. Any implementation I've been thinking on looks very "unoptimizied". For example, I'm trying with converting the array to a list of tuples, removing duplicates with set, and then converting to an array again: coordskeys = np.array(list(set([tuple(x) for x in coordskeys]))) Are there any existing solutions, so I do not reinvent the wheel? To make it clear, I'm looking for: >>> a = np.array([[1, 1], [2, 3], [1, 1], [5, 4], [2, 3]]) >>> unique_rows(a) array([[1, 1], [2, 3],[5, 4]]) BTW, I wanted to use just a list of tuples for it, but the lists were so big that they consumed my 4Gb RAM + 4Gb swap (numpy arrays are more memory efficient).
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