(df, missing="zeros", scaler="standard", **kwargs)
return s.fit(df)
Fit a sklearn scaler on a Data Frame and return the scaler.
        Valid options for the scaler are: standard, minmax, maxabs, robust, quantile
        Missing values must be dealt with before the scaling is applied. 
        Valid options specified through the missing parameter are: zeros, mean, median, mode
Fit a sklearn scaler on a Data Frame and return the scaler.
        Valid options for the scaler are: standard, minmax, maxabs, robust, quantile
        Missing values must be dealt with before the scaling is applied. 
        Valid options specified through the missing parameter are: zeros, mean, median, mode
Fit a sklearn scaler on a Data Frame and return the scaler . Valid options for the scaler are : standard minmax maxabs robust quantile Missing values must be dealt with before the scaling is applied . Valid options specified through the missing parameter are : zeros mean median mode
def get_scaler(df, missing="zeros", scaler="standard", **kwargs):
        """
        Fit a sklearn scaler on a Data Frame and return the scaler.
        Valid options for the scaler are: standard, minmax, maxabs, robust, quantile
        Missing values must be dealt with before the scaling is applied. 
        Valid options specified through the missing parameter are: zeros, mean, median, mode
        """

        scalers = {'standard':'StandardScaler', 'minmax':'MinMaxScaler', 'maxabs':'MaxAbsScaler',\
                   'robust':'RobustScaler', 'quantile':'QuantileTransformer'}

        s = getattr(preprocessing, scalers[scaler])
        s = s(**kwargs)

        df = Preprocessor.fillna(df, missing=missing)
        
        return s.fit(df)
def get_scaler ( df , missing = "zeros" , scaler = "standard" , * * kwargs ) : scalers = { 'standard' : 'StandardScaler' , 'minmax' : 'MinMaxScaler' , 'maxabs' : 'MaxAbsScaler' , 'robust' : 'RobustScaler' , 'quantile' : 'QuantileTransformer' } s = getattr ( preprocessing , scalers [ scaler ] ) s = s ( * * kwargs ) df = Preprocessor . fillna ( df , missing = missing ) return s . fit ( df )