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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def reduce_wind_speed(wind_speed, wind_efficiency_curve_name='dena_mean'):
 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 """

    # 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