to outliers.
RobustScaler : Perform robust standardization that removes the influence
    of outliers but does not put outliers and inliers on the same scale.

Notes
-----
NaNs are treated as missing values: disregarded in fit, and maintained in
transform.

Examples
--------
>>> import numpy as np
>>> from sklearn.preprocessing import QuantileTransformer
>>> rng = np.random.RandomState(0)
>>> X = np.sort(rng.normal(loc=0.5, scale=0.25, size=(25, 1)), axis=0)
>>> qt = QuantileTransformer(n_quantiles=10, random_state=0)
>>> qt.fit_transform(X)
array([...])
r(