docs: def rank1d(X, y=None, ax=None, algorithm='shapiro', features=None,
             orient='h', show_feature_names=True, **kwargs):
      """Scores each feature with the algorithm and ranks them in a bar plot.
  
      This helper function is a quick wrapper to utilize the Rank1D Visualizer
      (Transformer) for one-off analysis.
  
      Parameters
      ----------
      X : ndarray or DataFrame of shape n x m
          A matrix of n instances with m features
  
      y : ndarray or Series of length n
          An array or series of target or class values
  
      ax : matplotlib axes
          the axis to plot the figure on.
  
      algorithm : one of {'shapiro', }, default: 'shapiro'
          The ranking algorithm to use, default is 'Shapiro-Wilk.
  
      features : list
          A list of feature names to use.
          If a DataFrame is passed to fit and features is None, feature
          names are selected as the columns of the DataFrame.
  
      orient : 'h' or 'v'
          Specifies a horizontal or vertical bar chart.
  
      show_feature_names : boolean, default: True
          If True, the feature names are used to label the axis ticks in the
          plot.
  
      Returns
      -------
      ax : matplotlib axes
          Returns the axes that the parallel coordinates were drawn on.
  
      """
      # Instantiate the visualizer
      visualizer = Rank1D(ax, algorithm, features, orient, show_feature_names,
                          **kwargs)
  
      # Fit and transform the visualizer (calls draw)
      visualizer.fit(X, y, **kwargs)
      visualizer.transform(X)
  
      # Return the axes object on the visualizer
      return visualizer.ax