code: def wage(return_X_y=True):
      
      # y is real
      # recommend LinearGAM
      wage = pd.read_csv(PATH + '/wage.csv', index_col=0)
      if return_X_y:
          X = wage[['year', 'age', 'education']].values
          X[:,-1] = np.unique(X[:,-1], return_inverse=True)[1]
          y = wage['wage'].values
          return _clean_X_y(X, y)
      return wage
docs: wage dataset
  
      Parameters
      ----------
      return_X_y : bool,
          if True, returns a model-ready tuple of data (X, y)
          otherwise, returns a Pandas DataFrame
  
      Returns
      -------
      model-ready tuple of data (X, y)
          OR
      Pandas DataFrame
  
      Notes
      -----
      X contains the year, age and education of each sampled person.
      The education category has been transformed to integers.
  
      y contains the wage.
  
      Source:
      https://github.com/JWarmenhoven/ISLR-python/blob/master/Notebooks/Data/Wage.csv