As I'm sure I stated in the GItHub discussion, I strongly support adding these functions to NumPy. This logic is non-trivial to get right and is quite broadly useful.
These names also seem natural to me. On Mon, May 28, 2018 at 8:07 PM Eric Wieser <wieser.eric+nu...@gmail.com> wrote: > These functions provide a vectorized way of using one array to look up > items in another. In particular, they extend the 1d: > > a = np.array([4, 5, 6, 1, 2, 3]) > b = np.array(["four", "five", "six", "one", "two", "three"]) > i = a.argsort() > b_sorted = b[i] > > To work for higher-dimensions: > > a = np.array([[4, 1], [5, 2], [6, 3]]) > b = np.array([["four", "one"], ["five", "two"], ["six", "three"]]) > i = a.argsort(axis=1) > b_sorted = np.take_along_axis(b, i, axis=1) > > put_along_axis is the obvious but less useful dual to this operation, > inserting elements rather than extracting them. (Unlike put and take > which are not obvious duals). > > These have been merged in gh-11105 > <https://github.com/numpy/numpy/pull/11105>, but as a new addition this > probably should have gone by the mailing list first. > > There was a lack of consensus in gh-8714 > <https://github.com/numpy/numpy/pull/8714> about how best to generalize > to differing dimensions, so only the non-controversial case where the > indices and array have the same dimensions was implemented. > > These names were chosen to mirror apply_along_axis, which behaves > similarly. Do they seem reasonable? > > Eric > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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