Here is a quick benchmark between numpy's unique, unique1d and sasha's unique: x = rand(100000)*100 x = x.astype('i') %timeit unique(x) 10 loops, best of 3: 525 ms per loop %timeit unique_sasha(x) 100 loops, best of 3: 10.7 ms per loop timeit unique1d(x) 100 loops, best of 3: 12.6 ms per loop So I wonder what is the added value of unique? Could unique1d simply become unique ? Cheers, David P.S. I modified sasha's version to account for the case where all elements are identical, which returned an empty array. def unique_sasha(x): s = sort(x) r = empty(s.shape, float) r[:-1] = s[1:] r[-1] = NaN return s[r != s] 2006/7/3, Robert Cimrman <cimrman3@ntc.zcu.cz>:

Sasha wrote:

On 7/2/06, Norbert Nemec <Norbert.Nemec.list@gmx.de> wrote:

... Does anybody know about the internals of the python "set"? How is .keys() implemented? I somehow have really doubts about the efficiency of this method.

Set implementation (Objects/setobject.c) is a copy and paste job from dictobject with values removed. As a result it is heavily optimized for the case of string valued keys - a case that is almost irrelevant for numpy.

I think something like the following (untested, 1d only) will probably be much faster and sorted:

def unique(x): s = sort(x) r = empty_like(s) r[:-1] = s[1:] r[-1] = s[0] return s[r != s]

There are 1d array set operations like this already in numpy (numpy/lib/arraysetops.py - unique1d, ...)

r.

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