In the end, I’ve only the list comprehension to work as expected A = [0,0,1,3] B = np.arange(8) np.random.shuffle(B) I = [list(B).index(item) for item in A if item in B]
But Mark's and Sebastian's methods do not seem to work... > On 30 Dec 2015, at 19:51, Nicolas P. Rougier <nicolas.roug...@inria.fr> wrote: > > > Unfortunately, this does not handle repeated entries in a. > >> On 30 Dec 2015, at 19:40, Mark Miller <markperrymil...@gmail.com> wrote: >> >> I was not familiar with the .in1d function. That's pretty handy. >> >> Yes...it looks like numpy.where(numpy.in1d(b, a)) does what you need. >> >>>>> numpy.where(numpy.in1d(b, a)) >> (array([1, 2, 5, 7], dtype=int64),) >> It would be interesting to see the benchmarks. >> >> >> On Wed, Dec 30, 2015 at 10:17 AM, Nicolas P. Rougier >> <nicolas.roug...@inria.fr> wrote: >> >> Yes, it is the expected result. Thanks. >> Maybe the set(a) & set(b) can be replaced by np.where[np.in1d(a,b)], no ? >> >>> On 30 Dec 2015, at 18:42, Mark Miller <markperrymil...@gmail.com> wrote: >>> >>> I'm not 100% sure that I get the question, but does this help at all? >>> >>>>>> a = numpy.array([3,2,8,7]) >>>>>> b = numpy.array([1,3,2,4,5,7,6,8,9]) >>>>>> c = set(a) & set(b) >>>>>> c #contains elements of a that are in b (and vice versa) >>> set([8, 2, 3, 7]) >>>>>> indices = numpy.where([x in c for x in b])[0] >>>>>> indices #indices of b where the elements of a in b occur >>> array([1, 2, 5, 7], dtype=int64) >>> >>> -Mark >>> >>> >>> On Wed, Dec 30, 2015 at 6:45 AM, Nicolas P. Rougier >>> <nicolas.roug...@inria.fr> wrote: >>> >>> I’m scratching my head around a small problem but I can’t find a vectorized >>> solution. >>> I have 2 arrays A and B and I would like to get the indices (relative to B) >>> of elements of A that are in B: >>> >>>>>> A = np.array([2,0,1,4]) >>>>>> B = np.array([1,2,0]) >>>>>> print (some_function(A,B)) >>> [1,2,0] >>> >>> # A[0] == 2 is in B and 2 == B[1] -> 1 >>> # A[1] == 0 is in B and 0 == B[2] -> 2 >>> # A[2] == 1 is in B and 1 == B[0] -> 0 >>> >>> Any idea ? I tried numpy.in1d with no luck. >>> >>> >>> Nicolas >>> >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@scipy.org >>> https://mail.scipy.org/mailman/listinfo/numpy-discussion >>> >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@scipy.org >>> https://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> https://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> https://mail.scipy.org/mailman/listinfo/numpy-discussion > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
