The fit method is the primary drawing input for the
        visualization since it has both the X and y data required for the
        viz and the transform method does not.

        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

        kwargs : dict
            Pass generic arguments to the drawing method

        Returns
        -------
        self : instance
            Returns the instance of the transformer/visualizer

def fit(self, X, y=None, **kwargs):
        """
        The fit method is the primary drawing input for the
        visualization since it has both the X and y data required for the
        viz and the transform method does not.

        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

        kwargs : dict
            Pass generic arguments to the drawing method

        Returns
        -------
        self : instance
            Returns the instance of the transformer/visualizer
        """
        super(DataVisualizer, self).fit(X, y, **kwargs)

        # Store the classes for the legend if they're None.
        if self.classes_ is None:
            # TODO: Is this the most efficient method?
            self.classes_ = [str(label) for label in np.unique(y)]

        # Draw the instances
        self.draw(X, y, **kwargs)

        # Fit always returns self.
        return self