'Compute the median and quantiles to be used for scaling.
Parameters
X : array-like, shape [n_samples, n_features]
The data used to compute the median and quantiles
used for later scaling along the features axis.'

def fit(self, X, y=None):

    if sparse.issparse(X):
        raise TypeError('RobustScaler    cannot    be    fitted    on    sparse    inputs')
    X = self._check_array(X, self.copy)
    if self.with_centering:
        self.center_ = np.median(X, axis=0)
    if self.with_scaling:
        (q_min, q_max) = self.quantile_range
        if (not (0 <= q_min <= q_max <= 100)):
            raise ValueError(('Invalid    quantile    range:    %s' % str(self.quantile_range)))
        q = np.percentile(X, self.quantile_range, axis=0)
        self.scale_ = (q[1] - q[0])
        self.scale_ = _handle_zeros_in_scale(self.scale_, copy=False)
    return self
