josef.pkt wrote: >>> a = np.array([[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]]) >>> i = np.array([0, 1, 1, 0, 1]) >>> a[range(a.shape[0]), i] array([0, 3, 5, 6, 9]) >>> a[np.arange(a.shape[0]), i] array([0, 3, 5, 6, 9])
Thanks for all the tips. I guess I was hoping for something that could avoid having to generate np.arange(a.shape[0]), but >>> a[np.arange(a.shape[0]), i] sure is easy to understand. Is there maybe a more CPU and/or memory efficient way? I kind of like John Salvatier's idea: >>> np.choose(i, (a[:,0], a[:,1]) but that would need to be generalized to "a" of arbitrary columns. This could be done using split or vsplit: >>> np.choose(i, np.vsplit(a.T, a.shape[1]))[0] array([0, 3, 5, 6, 9]) That avoids having to generate an np.arange(), but looks kind of wordy. Is there a more compact way? Maybe this is better: >>> b, = i.choose(np.vsplit(a.T, a.shape[1])) >>> b array([0, 3, 5, 6, 9]) Ah, but I've just discovered a strange limitation of choose(): >>> a = np.arange(9*32) >>> a.shape = 9, 32 >>> i = np.random.randint(0, a.shape[1], size=a.shape[0]) >>> i array([ 1, 21, 23, 2, 30, 23, 20, 30, 17]) >>> b, = i.choose(np.vsplit(a.T, a.shape[1])) Traceback (most recent call last): File "<input>", line 1, in <module> ValueError: Need between 2 and (32) array objects (inclusive). Compare with: >>> a = np.arange(9*31) >>> a.shape = 9, 31 >>> i = np.random.randint(0, a.shape[1], size=a.shape[0]) >>> i array([14, 22, 18, 6, 1, 12, 8, 8, 30]) >>> b, = i.choose(np.vsplit(a.T, a.shape[1])) >>> b array([ 14, 53, 80, 99, 125, 167, 194, 225, 278]) So, the ValueError should really read "Need between 2 and 31 array object (inclusive)", should it not? Also, I can't seem to find this limitation in the docs for choose(). I guess I'll stick to using the np.arange(a.shape[0]) method. Martin _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion