Thanks for the quick answers. I think I will go with the .index and list comprehension. But if someone finds with a vectorised solution for the numpy 100 exercises...
Nicolas > On 30 Dec 2015, at 16:31, Benjamin Root <ben.v.r...@gmail.com> wrote: > > Maybe use searchsorted()? I will note that I have needed to do something like > this once before, and I found that the list comprehension form of calling > .index() for each item was faster than jumping through hoops to vectorize it > using searchsorted (needing to sort and then map the sorted indices to the > original indices), and was certainly clearer, but that might depend upon the > problem size. > > Cheers! > Ben Root > > On Wed, Dec 30, 2015 at 10:02 AM, Andy Ray Terrel <andy.ter...@gmail.com> > wrote: > Using pandas one can do: > > >>> A = np.array([2,0,1,4]) > >>> B = np.array([1,2,0]) > >>> s = pd.Series(range(len(B)), index=B) > >>> s[A].values > array([ 1., 2., 0., nan]) > > > > On Wed, Dec 30, 2015 at 8:45 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([0,2,0]) > >>> 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
