code: def reduce_wind_speed(wind_speed, wind_efficiency_curve_name='dena_mean'):
      r
      # Get wind efficiency curve
      wind_efficiency_curve = get_wind_efficiency_curve(
          curve_name=wind_efficiency_curve_name)
      # Reduce wind speed by wind efficiency
      reduced_wind_speed = wind_speed * np.interp(
          wind_speed, wind_efficiency_curve['wind_speed'],
          wind_efficiency_curve['efficiency'])
      return reduced_wind_speed
docs: r"""
      Reduces wind speed by a wind efficiency curve.
  
      The wind efficiency curves are provided in the windpowerlib and were
      calculated in the dena-Netzstudie II and in the work of Knorr
      (see [1]_ and [2]_).
  
      Parameters
      ----------
      wind_speed : pandas.Series or numpy.array
          Wind speed time series.
      wind_efficiency_curve_name : string
          Name of the wind efficiency curve. Use
          :py:func:`~.get_wind_efficiency_curve` to get all provided wind
          efficiency curves. Default: 'dena_mean'.
  
      Returns
      -------
      reduced_wind_speed : pd.Series or np.array
          `wind_speed` reduced by wind efficiency curve.
  
      References
      ----------
      .. [1] Kohler et.al.: "dena-Netzstudie II. Integration erneuerbarer
              Energien in die deutsche Stromversorgung im Zeitraum 2015 – 2020
              mit Ausblick 2025.", Deutsche Energie-Agentur GmbH (dena),
              Tech. rept., 2010, p. 101
      .. [2] Knorr, K.: "Modellierung von raum-zeitlichen Eigenschaften der
               Windenergieeinspeisung für wetterdatenbasierte
               Windleistungssimulationen". Universität Kassel, Diss., 2016,
               p. 124