text: from .inside_airbnb import InsideAirBnB
  import pandas as pd
  
  
  locations = InsideAirBnB()
  list_locations = list(locations.load_locations().get('name'))
  
  
  def neighbourhood_function(alpha):
      locations = InsideAirBnB()
      delta = locations.get_data(location_name=alpha, date=None, data_files=['data/listings.csv.gz'])
      epsilon = delta['listings-dat']['neighbourhood_cleansed']
      zeta = pd.DataFrame(epsilon.value_counts()).index
      return zeta
  
  
  # if __name__ == "__main__":
  # for x in beta:
  
  def get_at_it(location, geo=False):
      locations = InsideAirBnB()
      if geo:
          scepter = locations.get_data(location_name=location, date=None, data_files=['visualisations/listings.csv'])
          geo_df = scepter['listings-vis']
          eighty = locations.get_data(location_name=location, date=None, data_files=['visualisations/neighbourhoods.geojson'])
          #alpha = eighty['neighborhoods-geo']
          geo_df = geo_df.groupby(by=["neighbourhood"]).mean()
  
          return eighty['neighbourhoods-geo'], geo_df
      omega = locations.get_data(location_name=location, date=None, data_files=['data/listings.csv.gz'])
      kappa = omega['listings-dat']
  
      return kappa
  