code: def load_describe(column_series, additional_aggs=None):
      """
      Helper function for grabbing the output from :meth:`pandas:pandas.Series.describe` in a JSON serializable format
  
      :param column_series: data to describe
      :type column_series: :class:`pandas:pandas.Series`
      :return: JSON serializable dictionary of the output from calling :meth:`pandas:pandas.Series.describe`
      """
      desc = column_series.describe().to_frame().T
      code = [
          "# main statistics",
          "stats = df['{col}'].describe().to_frame().T".format(
              col=column_series.name
          ),
      ]
      if additional_aggs:
          for agg in additional_aggs:
              if agg == "mode":
                  mode = column_series.mode().values
                  desc["mode"] = np.nan if len(mode) > 1 else mode[0]
                  code.append(
                      (
                          "# mode\n"
                          "mode = df['{col}'].mode().values\n"
                          "stats['mode'] = np.nan if len(mode) > 1 else mode[0]"
                      ).format(col=column_series.name)
                  )
                  continue
              desc[agg] = getattr(column_series, agg)()
              code.append(
                  "# {agg}\nstats['{agg}'] = df['{col}'].{agg}()".format(
                      col=column_series.name, agg=agg
                  )
              )
      desc_f_overrides = {
          "I": lambda f, i, c: f.add_int(i, c, as_string=True),
          "F": lambda f, i, c: f.add_float(
              i, c, precision=4, as_string=True
          ),
      }
      desc_f = grid_formatter(
          grid_columns(desc),
          nan_display="nan",
          overrides=desc_f_overrides,
      )
      desc = desc_f.format_dict(next(desc.itertuples(), None))
      if "count" in desc:
          # pandas always returns 'count' as a float and it adds useless decimal points
          desc["count"] = desc["count"].split(".")[0]
      desc["total_count"] = json_int(len(column_series), as_string=True)
      missing_ct = column_series.isnull().sum()
      desc["missing_pct"] = json_float(
          (missing_ct / len(column_series) * 100).round(2)
      )
      desc["missing_ct"] = json_int(missing_ct, as_string=True)
      return desc, code
  
label_desc: Use image node_id 1 for calling a global function with example usage: load_describe(column_series, additional_aggs) and returns: desc,  code