I have following python method for an anova test:

    def anova(self, df, groupname, value, title, valueName=None):
        valueName = valueName or value
        if len(list(df[groupname].unique())) > 8:
            groupsplit = pd.qcut(df[groupname], q=8, precision=0, duplicates='drop')
        else:
            groupsplit = groupname
        groups = df.groupby(groupsplit)[value].apply(list)
        anova_results = f_oneway(*groups)
        # print('ANOVA Results:')
        # print('F-value:', anova_results.statistic)
        # print('p-value:', anova_results.pvalue)
        fig, ax = plt.subplots(figsize=(8, 6))
        sns.boxplot(x=groupsplit, y=value, data=df)
        plt.xlabel(title)
        plt.ylabel(valueName)
        plt.title(f"ANOVA - Influence of {title} on {valueName} (F={anova_results.statistic:.2f}, p={anova_results.pvalue:.2e})")
       

        # Adjust x-axis labels
        xtick_labels = [tick.get_text() for tick in ax.get_xticklabels()]
        adjusted_xtick_labels = []
        for i, label in enumerate(xtick_labels):
            if i % 2 == 1:
                label = '\n' + label  # Add a newline character to shift even labels down
            adjusted_xtick_labels.append(label)
        ax.set_xticklabels(adjusted_xtick_labels)
        
        plt.tight_layout()

        self.addAsset(f"{value}/anova-{groupname}-{value}")
        
i want to add a NAME_1 test that schows which subgroup have a significant diffrence in the mean, how can i implement this test and visualize it in the boxplot ? Respond with at most 350 words.
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