[Numpy-discussion] Adding take_along_axis and put_along_axis functions

[Stephan Hoyer]

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+numpy at 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
> ​
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