Exalted presences and superior intellects aside, the point is not hard to get: Motivational examples are everywhere.
Think about gridding physical problems expressed in cylindrical or spherical coordinates. The natural slices are not rectangles. You can use rectangular storage but only with O(n^3) waste.
More abstract solution spaces of math and physics do not usually lend themselves to rectangular treatments. (I understand finite element techniques and am not referring to those.) Again, rectangular storage is possible only with O(n^d) waste, where commonly d>3.
Granted one may overcome these issues with software development effort; that insight begs the question. I am looking for teaching software that already does so.
I agree that rectangular storage is easiest for software programmers and hence common. It is not easiest for solving all problems. Students should explore solutiuon spaces in a proper setting. So I just asked what numpy could do in this regard. Now I have the plain answer, and am grateful for it.