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
        """
        if is_dataframe(X):
            self.X = X.values
            if self.features_ is None:
                self.features_ = X.columns
        else:
            self.X = X

        self.y = y

        super(MissingDataVisualizer, self).fit(X, y, **kwargs)