old_contents: from bs4 import BeautifulSoup
  from park_api.util import convert_date
  from park_api.geodata import GeoData
  
  # This loads the geodata for this city if <city>.geojson exists in the same directory as this file.
  # No need to remove this if there's no geodata (yet), everything will still work.
  geodata = GeoData(__file__)
  
  # This function is called by the scraper and given the data of the page specified as source in geojson above.
  # It's supposed to return a dictionary containing everything the current spec expects. Tests will fail if it doesn't ;)
  def parse_html(html):
  
      # BeautifulSoup is a great and easy way to parse the html and find the bits and pieces we're looking for.
      soup = BeautifulSoup(html, "html.parser")
  
      data = {
          "last_updated": utc_now(),     # not found on site, so we use something else
          # URL for the page where the scraper can gather the data
          "lots": []
      }
      # for handling duplicate entries
      dataUniqe = dict()
  
      # find all entries:
      # suche <div class="houses">
      parking_houses = soup.find_all('div', class_='houses')
      for parking_group in parking_houses :
          parking_lots = parking_group.find_all('li') 
          for one_lot in parking_lots :
              parking_name = one_lot.find('a').text
              if ( not parking_name in dataUniqe ) :
                  dataUniqe[parking_name] = 1    # add this to the list
                  lot = geodata.lot(parking_name)
  
                  parking_state = 'open'
                  parking_free = 0
                  try :
                      parking_free = int(one_lot.find('span', class_='free-text').text.split()[0])
                  except :
                      parking_state = 'nodata'
  
                  data["lots"].append({
                      "name":     parking_name,
                      "free":     parking_free,
                      "total":    lot.total,
                      "address":  lot.address,
                      "coords":   lot.coords,
                      "state":    parking_state,
                      "lot_type": lot.type,
                      "id":       lot.id,
                      "forecast": False
                  })
  
      return data
  
  
new_contents: from bs4 import BeautifulSoup
  # from park_api.util import convert_date
  from park_api.geodata import GeoData
  from park_api.util import utc_now
  
  # This loads the geodata for this city if <city>.geojson exists in the same directory as this file.
  # No need to remove this if there's no geodata (yet), everything will still work.
  geodata = GeoData(__file__)
  
  # This function is called by the scraper and given the data of the page specified as source in geojson above.
  # It's supposed to return a dictionary containing everything the current spec expects. Tests will fail if it doesn't ;)
  def parse_html(html):
  
      # BeautifulSoup is a great and easy way to parse the html and find the bits and pieces we're looking for.
      soup = BeautifulSoup(html, "html.parser")
  
      data = {
          "last_updated": utc_now(),     # not found on site, so we use something else
          # URL for the page where the scraper can gather the data
          "lots": []
      }
      # for handling duplicate entries
      dataUniqe = dict()
  
      # find all entries:
      # suche <div class="houses">
      parking_houses = soup.find_all('div', class_='houses')
      for parking_group in parking_houses :
          parking_lots = parking_group.find_all('li') 
          for one_lot in parking_lots :
              parking_name = one_lot.find('a').text
              if ( not parking_name in dataUniqe ) :
                  dataUniqe[parking_name] = 1    # add this to the list
                  lot = geodata.lot(parking_name)
  
                  parking_state = 'open'
                  parking_free = 0
                  try :
                      parking_free = int(one_lot.find('span', class_='free-text').text.split()[0])
                  except :
                      parking_state = 'nodata'
  
                  data["lots"].append({
                      "name":     parking_name,
                      "free":     parking_free,
                      "total":    lot.total,
                      "address":  lot.address,
                      "coords":   lot.coords,
                      "state":    parking_state,
                      "lot_type": lot.type,
                      "id":       lot.id,
                      "forecast": False
                  })
  
      return data
  
  
subject: Update Aachen.py