old_contents: 
  
  def get_score_for_city(city, error_path):
      """reads in a city and the error_path and displays the score
  
      :param city: city for which the weather forecast is for
      :type string
      :param error_path: path to error data
      :type string
      :return: None
      """
  
      pass
  
  
  def load_error_data(city, provider, error_path):
      """
  
      :param city: city for which the weather forecast is for
      :type string
      :param provider: provider for which the weather forecast is for
      :type string
      :param error_path: path to error data
      :type string
      :return: dataFrame containing all errors for a city and provider
      """
  
      error_data = 0
      return error_data
  
  def get_score(dwd_data, forecast_data):
      """Gets a single pandas table rows of the dwd data and forecast_data and
      returns scalar error-values for each column
  
      :param dwd_data:  single row of a pandas table with columns = ['Provider','ref_date','city','pred_offset','Station ID', 'Date', 'Quality Level', 'Air Temperature', \
      'Vapor Pressure', 'Degree of Coverage', 'Air Pressure', 'Rel Humidity', \
      'Wind Speed', 'Max Air Temp', 'Min Air Temp', 'Min Groundlvl Temp', \
      'Max Wind Speed', 'Precipitation', 'Precipitation Ind', 'Hrs of Sun', \
      'Snow Depth']
      :type: dataframe
      :param forecast_data: single row of a pandas table with columns = ['Provider','ref_date','city','pred_offset','Station ID', 'Date', 'Quality Level', 'Air Temperature', \
      'Vapor Pressure', 'Degree of Coverage', 'Air Pressure', 'Rel Humidity', \
      'Wind Speed', 'Max Air Temp', 'Min Air Temp', 'Min Groundlvl Temp', \
      'Max Wind Speed', 'Precipitation', 'Precipitation Ind', 'Hrs of Sun', \
      'Snow Depth']
      :type: dataframe
      :return:list of differences between dwd and forecast (dwd-forecast)
      for the columns Air Temperature, Rel Humidity, Wind Speed, Max Air Temp, Min Air Temp
      Precipitation, Snow Depth
      """
  
      errors = []
      comparison_columns = ['Air Temperature', 'Rel Humidity', 'Wind Speed', 'Max Air Temp', 'Min Air Temp',
      'Precipitation', 'Snow Depth']
      for col in comparison_columns:
          errors.append(dwd_data[col]-forecast_data[col])
      return errors
  
  def get_data_dwd(city,date,dwd_path):
      """reads in the city, date and dwd_path and returns the data queried from the dwd path
  
      :param city: city for which the weather forecast is for
      :type string
      :param date: date for which the weather forecast is for
      :type datetime
      :param dwd_path: path to the corresponding dwd data
      :type string
      :return: dataFrame containing relevant dwd data
      """
      
      pass
new_contents: 
  
  def get_score_for_city(city, error_path):
      """reads in a city and the error_path and displays the score
  
      :param city: city for which the weather forecast is for
      :type string
      :param error_path: path to error data
      :type string
      :return: None
      """
  
      pass
  
  
  def load_error_data(city, provider, error_path):
      """
  
      :param city: city for which the weather forecast is for
      :type string
      :param provider: provider for which the weather forecast is for
      :type string
      :param error_path: path to error data
      :type string
      :return: dataFrame containing all errors for a city and provider
      """
  
      error_data = 0
      return error_data
  
  def get_score(dwd_data, forecast_data):
      """Gets a single pandas table rows of the dwd data and forecast_data and
      returns scalar error-values for each column
  
      :param dwd_data:  single row of a pandas table with columns = ['Provider','ref_date','city','pred_offset','Station ID', 'Date', 'Quality Level', 'Air Temperature', \
      'Vapor Pressure', 'Degree of Coverage', 'Air Pressure', 'Rel Humidity', \
      'Wind Speed', 'Max Air Temp', 'Min Air Temp', 'Min Groundlvl Temp', \
      'Max Wind Speed', 'Precipitation', 'Precipitation Ind', 'Hrs of Sun', \
      'Snow Depth']
      :type: dataframe
      :param forecast_data: single row of a pandas table with columns = ['Provider','ref_date','city','pred_offset','Station ID', 'Date', 'Quality Level', 'Air Temperature', \
      'Vapor Pressure', 'Degree of Coverage', 'Air Pressure', 'Rel Humidity', \
      'Wind Speed', 'Max Air Temp', 'Min Air Temp', 'Min Groundlvl Temp', \
      'Max Wind Speed', 'Precipitation', 'Precipitation Ind', 'Hrs of Sun', \
      'Snow Depth']
      :type: dataframe
      :return:list of differences between dwd and forecast (dwd-forecast)
      for the columns Air Temperature, Rel Humidity, Wind Speed, Max Air Temp, Min Air Temp
      Precipitation, Snow Depth
      """
  
      errors = []
      comparison_columns = ['Air Temperature', 'Rel Humidity', 'Wind Speed', 'Max Air Temp', 'Min Air Temp',
      'Precipitation', 'Snow Depth']
      for col in comparison_columns:
          errors.append(dwd_data[col]-forecast_data[col])
      return errors
  
  def get_data_dwd(city,date,dwd_path):
      """reads in the city, date and dwd_path and returns the data queried from the dwd path
  
      :param city: city for which the weather forecast is for
      :type string
      :param date: date for which the weather forecast is for
      :type datetime
      :param dwd_path: path to the corresponding dwd data
      :type string
      :return: dataFrame containing relevant dwd data
      """
  
      pass
  
  
  def get_date_forecast(city, provider, date, offset, forecast_dataframe):
      """Returns the row of the forecast_dataframe corresponding to the given city, provider, date
      and offset.
  
      :param city: city name
      :type string
      :param provider: provider name
      :type string
      :param date: date of the day for which to get the forecast
      type: datetime (python package datetime.datetime)
      :param offset: day of forecast minus day where forecast was made
      :type int
      :param forecast_dataframe:
      :type pandas dataframe
      :return: pandas dataframe row of forecast_dataframe corresponding to the
      given parameters
      """
      pass
  
  
  
  def update_forecasts(date, forecast_path, dwd_path, errors_path):
      pass
  
  
subject: Add unimplemented functions