I'm glad you bring this up, because this is something I'm not too sure about myself. The motivation for it is that when the input data is Gaussian, this scaling would cause the result to have a variance/std of 1 (In other words, for a Normal distribution, the iqr = 1.349*sigma), which is a theoretical nicety -- assuming most data is somewhat Gaussian. But it's entirely optional and I'm not sure whether to leave it in or not.

 It might also be misleading if users really do expect to have an std of 1 after scaling: even with data drawn from a proper Gaussian you need ~1k-10k datapoints until the `robust_scale`'d data reaches an std of 1.00 (= up to two significant digits).

Regarding the name you are probably right, `interquartile_scale` would be better (If the scaling stays in at all).