On Fri, Nov 20, 2009 at 01:21, - <[email protected]> wrote: > So if I have a 2-dimensional array, and I want to return the indexes > of all the elements that are larger than some value, then I just use > np.argwhere(d > 1) or something, where d is the array. However, is > there anything I can do that's similar, but return the indexes of the > elements that are larger than a value, where the value depends on the > column index? Like if the column index is less than 5 then do > np.argwhere(d > 1) and np.argwhere(d > 2) otherwise. I know I can > split up the array and then apply these on both arrays, but then I end > up with an unsorted list if I want the indexes to be sorted by rows, > so I'd have to deal with that also, which would take up a lot of time. > So if anyone knows what I should do, that would be great. Thanks.
Comparisons are just operators like +-/* and array broadcasting works on them, too. So, if d.shape == (N, 10), let's say, then you could do: np.argwhere(d > [1,1,1,1,1,2,2,2,2,2]) -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
