Python BoxPlot with Bokeh - Multiple data points displayed

def bokeh_tukey_summary_boxplot(self): #
        from bokeh.plotting import figure, show, output_file
        from bokeh.embed import components
        """
        Some Py code to get the Current dataset_name
        """
        dataset_name = dataset_name

        from sqlalchemy import create_engine
        user = settings.DATABASES['default']['USER']
        password = settings.DATABASES['default']['PASSWORD']
        database_name = settings.DATABASES['default']['NAME']
        database_url = 'postgresql://{user}:{password}@localhost:5432/{database_name}'.format(
            user=user,
            password=password,
            database_name=database_name,
        )
        engine = create_engine(database_url, echo=False)
        schema_default_public = "public"
        sql_command = "SELECT * FROM {} ;".format(str(dataset_name)) #
        df_for_bokeh = pd.read_sql(sql_command,engine)
        groups = df_for_bokeh.groupby('segments')
        for key, item in groups:
            print(groups.get_group(key), "\n\n")
        q1 = groups.quantile(q=0.25)
        q2 = groups.quantile(q=0.5)
        q3 = groups.quantile(q=0.75)
        iqr = q3 - q1
        upper = q3 + 1.5*iqr
        lower = q1 - 1.5*iqr
        col_names_fromPSQL = list(df_for_bokeh) #
        ls_SeriesName = []
        ls_SeriesUnqCnts = []
        for k in range(len(col_names_fromPSQL)):
            series_name = str(col_names_fromPSQL[k])
            ls_SeriesName.append(series_name)
            unq_values_list = df_for_bokeh[series_name].unique()
            ls_SeriesUnqCnts.append(len(unq_values_list))
        df_calcUnq = pd.DataFrame({'ls_SeriesName':ls_SeriesName,'ls_SeriesUnqCnts':ls_SeriesUnqCnts})
        min_valIndex = df_calcUnq['ls_SeriesUnqCnts'].idxmin()
        col_with_CategoricalValues = df_calcUnq.iloc[min_valIndex]['ls_SeriesName']
        unq_values_list_final = df_for_bokeh[col_with_CategoricalValues].unique()
        list_of_other_Cols = []
        for k in range(len(col_names_fromPSQL)):
            if str(col_names_fromPSQL[k]) == col_with_CategoricalValues:
                pass
            else:
                list_of_other_Cols.append(str(col_names_fromPSQL[k]))
        # find the outliers for each category
        def outliers(group):
            unq_segments_list = df_for_bokeh[col_with_CategoricalValues].unique() #
            values_col = list_of_other_Cols[0] #
            for k in range(len(unq_segments_list)):
                cat = unq_segments_list[k] #
            return group[(group.height > upper.loc[cat][values_col])][values_col]
        out = groups.apply(outliers).dropna()
        # prepare outlier data for plotting, we need coordinates for every outlier.
        if not out.empty:
            outx = []
            outy = []
            for keys in out.index:
                outx.append(keys[0])
                outy.append(out.loc[keys[0]].loc[keys[1]])

        # import bokeh.plotting as bk
        # from bokeh.models import HoverTool

        # source = bk.ColumnDataSource(df_for_bokeh)
        # hover = HoverTool(
        #             tooltips = [
        #                 ("index1", "@segments")
        #                         ]
        #                     )
        TOOLTIPS = """
            <div style="background-color:orange;">
                <div>
                    <span style="font-size: 15px; color: #966;">@name</span>
                </div>

                <div>
                    <span style="font-size: 10px; color: black;">($y{int})</span>
                </div>
            </div>
        """

        unq_segments_list = df_for_bokeh['segments'].unique() #
        cats = unq_segments_list
        p = figure(tools="", background_fill_color="#efefef", x_range=cats,plot_width=195, plot_height=550,tooltips=TOOLTIPS)
        # if no outliers, shrink lengths of stems to be no longer than the minimums or maximums
        qmin = groups.quantile(q=0.00)
        qmax = groups.quantile(q=1.00)
        upper.height = [min([x,y]) for (x,y) in zip(list(qmax.loc[:,'height']),upper.height)]
        lower.height = [max([x,y]) for (x,y) in zip(list(qmin.loc[:,'height']),lower.height)]
        p.segment(cats, upper.height, cats, q3.height, line_color="black") #
        p.segment(cats, lower.height, cats, q1.height, line_color="black")
        # boxes
        p.vbar(cats, 0.7, q2.height, q3.height, fill_color="#E08E79", line_color="black")
        p.vbar(cats, 0.7, q1.height, q2.height, fill_color="#3B8686", line_color="black")
        # whiskers (almost-0 height rects simpler than segments)
        p.rect(cats, lower.height, 0.2, 0.01, line_color="black")
        #height
        p.rect(cats, upper.height, 0.2, 0.01, line_color="black")
        # outliers
        if not out.empty:
            p.circle(outx, outy, size=6, color="#F38630", fill_alpha=0.6)
        p.xgrid.grid_line_color = None
        p.ygrid.grid_line_color = "white"
        p.grid.grid_line_width = 2
        p.xaxis.major_label_text_font_size="12pt"
        p.toolbar.logo = None
        p.toolbar_location = None

        js_boxplot, div_boxplot = components(p)
        cdn_js_boxplot=CDN.js_files[0]
        cdn_css_boxplot=CDN.css_files[0]

        return js_boxplot,div_boxplot ,cdn_js_boxplot,cdn_css_boxplot