code: def coal(return_X_y=True):
      
      # y is counts
      # recommend PoissonGAM
      coal = pd.read_csv(PATH + '/coal.csv', index_col=0)
      if return_X_y:
          y, x = np.histogram(coal.values, bins=150)
          X = x[:-1] + np.diff(x)/2 # get midpoints of bins
          return _clean_X_y(X, y)
      return coal
docs: coal-mining accidents 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
      -----
      The (X, y) tuple is a processed version of the otherwise raw DataFrame.
  
      A histogram of 150 bins has been computed describing the number accidents per year.
  
      X contains the midpoints of histogram bins.
      y contains the count in each histogram bin.
  
      Source:
      https://vincentarelbundock.github.io/Rdatasets/doc/boot/coal.html