text: from sklearn.pipeline import Pipeline, FeatureUnion
  from sklearn.impute import SimpleImputer
  from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
  from .transformation_utils import __DataFrameSelectorByIdx, __ClassReducer
  
  
  def NumericalPipeline(numeric_indexes):
      return Pipeline([
          ('selector', __DataFrameSelectorByIdx(numeric_indexes)),
          ('imputer', SimpleImputer(strategy="median")),
          # ('min_max_scaler', MinMaxScaler()),
          ('std_scaler', StandardScaler())
      ])
  
  
  def CategoricalPipeline(categorical_indexes):
      return Pipeline([
          ('selector', __DataFrameSelectorByIdx(
              categorical_indexes, acceptable_missing_threshold=3.35)),
          ('imputer', SimpleImputer(strategy='most_frequent')),
          ('class_reducer', __ClassReducer(min_class_frequency=0.05)),
          ('cat_encoder', OneHotEncoder(sparse=False))
      ])
  