(df, missing="zeros", scaler="StandardScaler", **kwargs)
return s.fit(df)
Fit a sklearn scaler on a Data Frame and return the scaler.
    Valid options for the scaler are: StandardScaler, MinMaxScaler, MaxAbsScaler, RobustScaler, QuantileTransformer
    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: StandardScaler, MinMaxScaler, MaxAbsScaler, RobustScaler, QuantileTransformer
    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 : StandardScaler MinMaxScaler MaxAbsScaler RobustScaler QuantileTransformer 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="StandardScaler", **kwargs):
    """
    Fit a sklearn scaler on a Data Frame and return the scaler.
    Valid options for the scaler are: StandardScaler, MinMaxScaler, MaxAbsScaler, RobustScaler, QuantileTransformer
    Missing values must be dealt with before the scaling is applied. 
    Valid options specified through the missing parameter are: zeros, mean, median, mode
    """

    s = getattr(preprocessing, scaler)
    s = s(**kwargs)

    df = fillna(df, method=missing)
    
    return s.fit(df)
def get_scaler ( df , missing = "zeros" , scaler = "StandardScaler" , * * kwargs ) : s = getattr ( preprocessing , scaler ) s = s ( * * kwargs ) df = fillna ( df , method = missing ) return s . fit ( df )