docs: def scatterviz(X,
                 y=None,
                 ax=None,
                 features=None,
                 classes=None,
                 color=None,
                 colormap=None,
                 markers=None,
                 alpha=1.0,
                 **kwargs):
      """Displays a bivariate scatter plot.
  
      This helper function is a quick wrapper to utilize the ScatterVisualizer
      (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, default: None
          An array or series of target or class values
  
      ax : matplotlib axes, default: None
          The axes to plot the figure on.
  
      features : list of strings, default: None
          The names of two features or columns.
          More than that will raise an error.
  
      classes : list of strings, default: None
          The names of the classes in the target
  
      color : list or tuple of colors, default: None
          Specify the colors for each individual class
  
      colormap : string or matplotlib cmap, default: None
          Sequential colormap for continuous target
  
      markers : iterable of strings, default: ,+o*vhd
          Matplotlib style markers for points on the scatter plot points
  
      alpha : float, default: 1.0
          Specify a transparency where 1 is completely opaque and 0 is completely
          transparent. This property makes densely clustered points more visible.
  
      Returns
      -------
      ax : matplotlib axes
          Returns the axes that the parallel coordinates were drawn on.
      """
      # Instantiate the visualizer
      visualizer = ScatterVisualizer(ax=ax, features=features, classes=classes,
                                     color=color, colormap=colormap,
                                     markers=markers, alpha=alpha, **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