DCPY.ROBUSTSCALER(with_centering, with_scaling, quantile_range_min, quantile_range_max, column)
The Robust Scaler scales data according to the interquartile range and removes the median. It is a better alternative than the Standard Scaler (removing the mean and scaling to unit variance) in case of a higher number of outliers.
Parameters
 with_centering – Specifies if the data needs to be centered, Boolean (for example, True).
 with_scaling – Specifies if the data needs to be scaled to the interquartile range, Boolean (for example, True).
 quantile_range_min – Lower bound of the IQR used for scaling, float (for example, 25).
 quantile_range_max – Upper bound of the IQR used for scaling, float (for example, 75).

columns – Dataset column or custom calculation.
Example: DCPY.ROBUSTSCALER(True, True, 25, 75, [Discount])
Input data
 Numeric column.
 Rows containing missing values are dropped before calculations.
Result
 A numeric column with transformed values with the same length as the input column.
 Missing values are on the same indices as in the input column.
Key usage points
 Use it when the data contains a large number of outliers.
Example
The following example shows how the car weight and fuel economy (mpg) are scaled using the following functions:

DCPY.ROBUSTSCALER(True, True, 25, 75, [MPG])

DCPY.ROBUSTSCALER(True, True, 25, 75, [WT])
The two scaled values are visualized in the Butterfly visualization.
For the whole list of algorithms, see Data science builtin algorithms.