Test failure:

AttributeError: 'RobustScaler' object has no attribute 'center_'

Fixed now, sorry that it even happened, I only ran the sklearn.preprocessing specific tests before.

is this ready to review?

Yes it is

Pinging @amueller @ogrisel Maybe you want to have a look?

Yes, but probably only after he beta release is cut on Friday.

ping @amueller is now a good time to remind you about this PR?

It is always a good time to remind me ;) I'll try to have a look.

If you like you could add a plot_ example that shows samples from a 2d gaussian with outliers, how it gets transformed using StandardScaler (into something weird) and how using RobustScaler (a ball + outliers). Not required but I think this would be very illustrative.

Apart from my minor nitpicks this looks great. Sorry for the lack of feedback previously.

I am not sure I understand the parametrization here. So we are always using 1/4 3/4 quantiles and then scale them? Is that a standard practice? [sorry I rarely do robust statistics]

Long answer outside of the affected code lines so the answer stays visible for future reviewers: Yes, using the 0.25-0.75 (the "interquartile range") is the most common option, although others might make sense in some occasions (e.g. Wikipedia also meantions the interdecale range 0.1-0.9 as relevant). We could make the quantiles a user-definable parameter that just defaults to 0.25-0.75, to cover the more general case. The only question is if it's worth the added complexity for the user. I've personally never needed anything more than IQR, but maybe having the option might be nice?

@agramfort Thanks for taking the time to look at this. I've fixed the mistakes you pointed out, and went over the docstrings in data.py again and removed the repeated words/phrases. If you could look it over once more now, that'd be great.

ok LGTM

one last review?

Thanks for looking it over. Do PRs need 3 reviews before being accepted now?

Ok, I find that the issues raised by @agramfort are not major ones. I think that this can be merged as such.

However :), the median and the inter quartile range are very robust estimators but also very noisy. Using a trimmed mean, both on the raw data to estimate center location, and on the squared distances to the center, to estimate spread, is in general a much better strategy with a continuous parameter to interpolate between mean and median.

I would really love a PR adding such a behavior to the robust scaler. It worries me that anybody wanting to do robust scaling will right now have also very noisy scaling.

I never heard of the trimmed mean being more robust, could you name any references I could read up on?

Merged. Thanks @untom.
Would you like to do the MaxAbsScaler?

Nice! Thanks to the reviewers for their comments.

@amueller: I am fairly busy until the 5th, so most likely I will only find time to work on this afterwards.

I smell nips ;) Good luck!

In such cases, the median and the interquartile range often give better results.

If there are papers to support this claim, it would be nice to add them to the references.

Thanks @untom for this contribution. I added a what's new entry here: 549ecae.

Please feel free to open a new PR to update it to use your real name instead of your github nickname. You might also want to update the authors list in the header of the sklearn/preprocessing/data.py file.