This next part of code does exactly what I want it to do. dem_rows and dem_cols contain float values for a number of things i can identify in an image, but i need to get the nearest pixel for each of them, and than to make sure I only get the unique points, and no duplicates. The problem is that this code is ugly and as far as I get it, as unpythonic as it gets. If there would be a pure-numpy-solution (without for-loops) that would be even better. # next part is to make sure that we get the rounding done correctly, and than to get the integer part out of it # without the annoying floatingpoint-error, and without duplicates fielddic={} for i in range(len(dem_rows)): # here comes the ugly part: abusing the fact that i overwrite dictionary keys if I get duplicates fielddic[int(round(dem_rows[i]) + 0.1), int(round(dem_cols[i]) + 0.1)] = None # also very ugly: to make two arrays of integers out of the first and second part of the keys field_rows = numpy.zeros((len(fielddic.keys())), int) field_cols = numpy.zeros((len(fielddic.keys())), int) for i, (r, c) in enumerate(fielddic.keys()): field_rows[i] = r field_cols[i] = c