code: def interpolate(self, column_hours, method='linear', limit=24, limit_direction='both', **kwargs):
          
          kwargs = dict(kwargs, method=method, limit=limit, limit_direction=limit_direction)
          data = melodist.util.prepare_interpolation_data(self.data_daily, column_hours)
          return data.interpolate(**kwargs)
docs: Wrapper function for ``pandas.Series.interpolate`` that can be used to
          "disaggregate" values using various interpolation methods.
  
          Parameters
          ----------
          column_hours : dict
              Dictionary containing column names in ``data_daily`` and the hour
              values they should be associated to.
  
          method, limit, limit_direction, **kwargs
              These parameters are passed on to ``pandas.Series.interpolate``.
  
          Examples
          --------
          Assume that ``mystation.data_daily.T7``, ``mystation.data_daily.T14``,
          and ``mystation.data_daily.T19`` contain air temperature measurements
          taken at 07:00, 14:00, and 19:00.
          We can use the interpolation functions provided by pandas/scipy to derive
          hourly values:
  
          >>> mystation.data_hourly.temp = mystation.interpolate({'T7': 7, 'T14': 14, 'T19': 19}) # linear interpolation (default)
          >>> mystation.data_hourly.temp = mystation.interpolate({'T7': 7, 'T14': 14, 'T19': 19}, method='cubic') # cubic spline