def missing_dispersion(X, y=None, ax=None, classes=None, alpha=0.5, marker="|", **kwargs):
    """
    The Missing Values Dispersion visualizer shows the locations of missing (nan)
    values in the feature dataset by the order of the index.

    When y targets are supplied to fit, the output dispersion plot is color
    coded according to the target y that the element refers to.

    Parameters
    ----------
    alpha : float, default: 0.5
        A value for bending elments with the background.

    marker : matplotlib marker, default: |
        The marker used for each element coordinate in the plot

    classes : list, default: None
        A list of class names for the legend.
        If classes is None and a y value is passed to fit then the classes
        are selected from the target vector.

    kwargs : dict
        Keyword arguments that are passed to the base class and may influence
        the visualization as defined in other Visualizers.

    Attributes
    ----------
    features_ : np.array
        The feature labels ranked according to their importance

    classes_ : np.array
        The class labels for each of the target values

    Examples
    --------

    >>> from yellowbrick.contrib.missing import missing_dispersion
    >>> visualizer = missing_dispersion(X, y=y)

    """
    # Instantiate the visualizer
    visualizer = MissingValuesDispersion(
        ax=ax, classes=classes, alpha=alpha, marker=marker, **kwargs
    )

    # Fit and transform the visualizer (calls draw)
    visualizer.fit(X, y)
    visualizer.poof()

    # Return the axes object on the visualizer
    return visualizer.ax
The Missing Values Dispersion visualizer shows the locations of missing (nan)
    values in the feature dataset by the order of the index.

    When y targets are supplied to fit, the output dispersion plot is color
    coded according to the target y that the element refers to.

    Parameters
    ----------
    alpha : float, default: 0.5
        A value for bending elments with the background.

    marker : matplotlib marker, default: |
        The marker used for each element coordinate in the plot

    classes : list, default: None
        A list of class names for the legend.
        If classes is None and a y value is passed to fit then the classes
        are selected from the target vector.

    kwargs : dict
        Keyword arguments that are passed to the base class and may influence
        the visualization as defined in other Visualizers.

    Attributes
    ----------
    features_ : np.array
        The feature labels ranked according to their importance

    classes_ : np.array
        The class labels for each of the target values

    Examples
    --------

    >>> from yellowbrick.contrib.missing import missing_dispersion
    >>> visualizer = missing_dispersion(X, y=y)