How to use the autovizwidget.autovizwidget.widget.encoding.Encoding.y_agg_max function in autovizwidget

def display_dataframe(df):
    selected_x = select_x(df)
    selected_y = select_y(df, selected_x)
    encoding = Encoding(chart_type=Encoding.chart_type_table, x=selected_x, y=selected_y,
                        y_aggregation=Encoding.y_agg_max)
    return AutoVizWidget(df, encoding)

# over empty dataframes. We're just going to explicitly block against that here.
        if len(df) == 0:
            raise InvalidEncodingError("Cannot display graph for an empty data set.")

        try:
            df_grouped = df.groupby(x_column)
        except TypeError:
            raise InvalidEncodingError("Cannot group by X column '{}' because of its type: '{}'."
                                       .format(df[x_column].dtype))
        else:
            try:
                if y_aggregation == Encoding.y_agg_avg:
                    df_transformed = df_grouped.mean()
                elif y_aggregation == Encoding.y_agg_min:
                    df_transformed = df_grouped.min()
                elif y_aggregation == Encoding.y_agg_max:
                    df_transformed = df_grouped.max()
                elif y_aggregation == Encoding.y_agg_sum:
                    df_transformed = df_grouped.sum()
                elif y_aggregation == Encoding.y_agg_count:
                    df_transformed = df_grouped.count()
                else:
                    raise ValueError("Y aggregation '{}' not supported.".format(y_aggregation))
            except (DataError, ValueError) as err:
                raise InvalidEncodingError("Cannot aggregate column '{}' with aggregation function '{}' because:\n\t'{}'."
                                           .format(y_column, y_aggregation, err))
            except TypeError:
                raise InvalidEncodingError("Cannot aggregate column '{}' with aggregation function '{}' because the type\n"
                                           "cannot be aggregated over."
                                           .format(y_column, y_aggregation))
            else:
                df_transformed = df_transformed.reset_index()

# Y
        options_y_view = {text(i): text(i) for i in self.df.columns}
        options_y_view["-"] = None
        y_column_view = self.ipywidget_factory.get_dropdown(options=options_y_view,
                                                            description="Y", value=self.encoding.y)
        y_column_view.on_trait_change(self._y_changed_callback, 'value')
        y_column_view.layout.width = "200px"

        # Y aggregator
        value_for_view = self._get_value_for_aggregation(self.encoding.y_aggregation)
        self.y_agg_view = self.ipywidget_factory.get_dropdown(
            options={"-": Encoding.y_agg_none,
                     Encoding.y_agg_avg: Encoding.y_agg_avg,
                     Encoding.y_agg_min: Encoding.y_agg_min,
                     Encoding.y_agg_max: Encoding.y_agg_max,
                     Encoding.y_agg_sum: Encoding.y_agg_sum,
                     Encoding.y_agg_count: Encoding.y_agg_count},
            description="Func.",
            value=value_for_view)
        self.y_agg_view.on_trait_change(self._y_agg_changed_callback, 'value')
        self.y_agg_view.layout.width = "200px"

        # Y view
        self.y_view = self.ipywidget_factory.get_hbox()
        self.y_view.children = [y_column_view, self.y_agg_view]

        # Logarithmic X axis
        self.logarithmic_x_axis = self.ipywidget_factory.get_checkbox(
            description="Log scale X", value=encoding.logarithmic_x_axis)
        self.logarithmic_x_axis.on_trait_change(self._logarithmic_x_callback, "value")