def interaction_plot(x, trace, response, func=np.mean, ax=None, plottype='b',
                     xlabel=None, ylabel=None, colors=[], markers=[],
                     linestyles=[], legendloc='best', legendtitle=None,
                     **kwargs):
    """
    Interaction plot for factor level statistics.

    Note. If categorial factors are supplied levels will be internally
    recoded to integers. This ensures matplotlib compatiblity.

    uses pandas.DataFrame to calculate an `aggregate` statistic for each
    level of the factor or group given by `trace`.

    Parameters
    ----------
    x : array-like
        The `x` factor levels constitute the x-axis. If a `pandas.Series` is
        given its name will be used in `xlabel` if `xlabel` is None.
    trace : array-like
        The `trace` factor levels will be drawn as lines in the plot.
        If `trace` is a `pandas.Series` its name will be used as the
        `legendtitle` if `legendtitle` is None.
    response : array-like
        The reponse or dependent variable. If a `pandas.Series` is given
        its name will be used in `ylabel` if `ylabel` is None.
    func : function
        Anything accepted by `pandas.DataFrame.aggregate`. This is applied to
        the response variable grouped by the trace levels.
    plottype : str {'line', 'scatter', 'both'}, optional
        The type of plot to return. Can be 'l', 's', or 'b'
    ax : axes, optional
        Matplotlib axes instance
    xlabel : str, optional
        Label to use for `x`. Default is 'X'. If `x` is a `pandas.Series` it
        will use the series names.
    ylabel : str, optional
        Label to use for `response`. Default is 'func of response'. If
        `response` is a `pandas.Series` it will use the series names.
    colors : list, optional
        If given, must have length == number of levels in trace.
    linestyles : list, optional
        If given, must have length == number of levels in trace.
    markers : list, optional
        If given, must have length == number of lovels in trace
    kwargs
        These will be passed to the plot command used either plot or scatter.
        If you want to control the overall plotting options, use kwargs.

    Returns
    -------
    fig : Figure
        The figure given by `ax.figure` or a new instance.

    Examples
    --------
    >>> import numpy as np
    >>> np.random.seed(12345)
    >>> weight = np.random.randint(1,4,size=60)
    >>> duration = np.random.randint(1,3,size=60)
    >>> days = np.log(np.random.randint(1,30, size=60))
    >>> fig = interaction_plot(weight, duration, days,
    ...             colors=['red','blue'], markers=['D','^'], ms=10)
    >>> import matplotlib.pyplot as plt
    >>> plt.show()

    .. plot::

       import numpy as np
       from statsmodels.graphics.factorplots import interaction_plot
       np.random.seed(12345)
       weight = np.random.randint(1,4,size=60)
       duration = np.random.randint(1,3,size=60)
       days = np.log(np.random.randint(1,30, size=60))
       fig = interaction_plot(weight, duration, days,
                   colors=['red','blue'], markers=['D','^'], ms=10)
       import matplotlib.pyplot as plt
       #plt.show()
    """

    from pandas import DataFrame
    fig, ax = utils.create_mpl_ax(ax)

    response_name = ylabel or getattr(response, 'name', 'response')
    ylabel = '%s of %s' % (get_function_name(func), response_name)
    xlabel = xlabel or getattr(x, 'name', 'X')
    legendtitle = legendtitle or getattr(trace, 'name', 'Trace')

    ax.set_ylabel(ylabel)
    ax.set_xlabel(xlabel)

    x_values = x_levels = None
    if isinstance(x[0], str):
        x_levels = [l for l in np.unique(x)]
        x_values = lrange(len(x_levels))
        x = _recode(x, dict(zip(x_levels, x_values)))

    data = DataFrame(dict(x=x, trace=trace, response=response))
    plot_data = data.groupby(['trace', 'x']).aggregate(func).reset_index()

    # return data
    # check plot args
    n_trace = len(plot_data['trace'].unique())

    if linestyles:
        try:
            assert len(linestyles) == n_trace
        except AssertionError as err:
            raise ValueError("Must be a linestyle for each trace level")
    else:  # set a default
        linestyles = ['-'] * n_trace
    if markers:
        try:
            assert len(markers) == n_trace
        except __HOLE__ as err:
            raise ValueError("Must be a linestyle for each trace level")
    else:  # set a default
        markers = ['.'] * n_trace
    if colors:
        try:
            assert len(colors) == n_trace
        except AssertionError as err:
            raise ValueError("Must be a linestyle for each trace level")
    else:  # set a default
        #TODO: how to get n_trace different colors?
        colors = rainbow(n_trace)

    if plottype == 'both' or plottype == 'b':
        for i, (values, group) in enumerate(plot_data.groupby(['trace'])):
            # trace label
            label = str(group['trace'].values[0])
            ax.plot(group['x'], group['response'], color=colors[i],
                    marker=markers[i], label=label,
                    linestyle=linestyles[i], **kwargs)
    elif plottype == 'line' or plottype == 'l':
        for i, (values, group) in enumerate(plot_data.groupby(['trace'])):
            # trace label
            label = str(group['trace'].values[0])
            ax.plot(group['x'], group['response'], color=colors[i],
                    label=label, linestyle=linestyles[i], **kwargs)
    elif plottype == 'scatter' or plottype == 's':
        for i, (values, group) in enumerate(plot_data.groupby(['trace'])):
            # trace label
            label = str(group['trace'].values[0])
            ax.scatter(group['x'], group['response'], color=colors[i],
                    label=label, marker=markers[i], **kwargs)

    else:
        raise ValueError("Plot type %s not understood" % plottype)
    ax.legend(loc=legendloc, title=legendtitle)
    ax.margins(.1)

    if all([x_levels, x_values]):
        ax.set_xticks(x_values)
        ax.set_xticklabels(x_levels)
    return fig
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/graphics/factorplots.py/interaction_plot