docs: def import_lv_grid_districts(self, session, lv_stations):
          """Imports all lv grid districts within given load area
  
          Parameters
          ----------
          session : sqlalchemy.orm.session.Session
              Database session
  
          Returns
          -------
          lv_grid_districts: :pandas:`pandas.DataFrame<dataframe>`
              Table of lv_grid_districts
          """
  
          # get ding0s' standard CRS (SRID)
          srid = str(int(cfg_ding0.get('geo', 'srid')))
          # SET SRID 3035 to achieve correct area calculation of lv_grid_district
          # srid = '3035'
  
          gw2kw = 10 ** 6  # load in database is in GW -> scale to kW
  
          # 1. filter grid districts of relevant load area
          lv_grid_districs_sqla = session.query(
              self.orm['orm_lv_grid_district'].mvlv_subst_id,
              self.orm['orm_lv_grid_district'].la_id,
              self.orm['orm_lv_grid_district'].zensus_sum.label('population'),
              (self.orm[
                   'orm_lv_grid_district'].sector_peakload_residential * gw2kw).
                  label('peak_load_residential'),
              (self.orm['orm_lv_grid_district'].sector_peakload_retail * gw2kw).
                  label('peak_load_retail'),
              (self.orm[
                   'orm_lv_grid_district'].sector_peakload_industrial * gw2kw).
                  label('peak_load_industrial'),
              (self.orm[
                   'orm_lv_grid_district'].sector_peakload_agricultural * gw2kw).
                  label('peak_load_agricultural'),
              ((self.orm['orm_lv_grid_district'].sector_peakload_residential
                + self.orm['orm_lv_grid_district'].sector_peakload_retail
                + self.orm['orm_lv_grid_district'].sector_peakload_industrial
                + self.orm['orm_lv_grid_district'].sector_peakload_agricultural)
               * gw2kw).label('peak_load'),
              func.ST_AsText(func.ST_Transform(
                  self.orm['orm_lv_grid_district'].geom, srid)).label('geom'),
              self.orm['orm_lv_grid_district'].sector_count_residential,
              self.orm['orm_lv_grid_district'].sector_count_retail,
              self.orm['orm_lv_grid_district'].sector_count_industrial,
              self.orm['orm_lv_grid_district'].sector_count_agricultural,
              (self.orm[
                   'orm_lv_grid_district'].sector_consumption_residential * gw2kw). \
                  label('sector_consumption_residential'),
              (self.orm['orm_lv_grid_district'].sector_consumption_retail * gw2kw). \
                  label('sector_consumption_retail'),
              (self.orm[
                  'orm_lv_grid_district'].sector_consumption_industrial * gw2kw). \
                  label('sector_consumption_industrial'),
              (self.orm[
                  'orm_lv_grid_district'].sector_consumption_agricultural * gw2kw). \
                  label('sector_consumption_agricultural'),
              self.orm['orm_lv_grid_district'].mvlv_subst_id). \
              filter(self.orm['orm_lv_grid_district'].mvlv_subst_id.in_(
              lv_stations.index.tolist())). \
              filter(self.orm['version_condition_lvgd'])
  
          # read data from db
          lv_grid_districts = pd.read_sql_query(lv_grid_districs_sqla.statement,
                                                session.bind,
                                                index_col='mvlv_subst_id')
  
          lv_grid_districts[
              ['sector_count_residential',
               'sector_count_retail',
               'sector_count_industrial',
               'sector_count_agricultural']] = lv_grid_districts[
              ['sector_count_residential',
               'sector_count_retail',
               'sector_count_industrial',
               'sector_count_agricultural']].fillna(0)
  
          return lv_grid_districts