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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 first_location_of_minimum(x):
 """ Returns the first location of the minimal value of x. The position is calculated relatively to the length of x. :param x: the time series to calculate the feature of :type x: numpy.ndarray :return: the value of this feature :return type: float """

    if not isinstance(x, (np.ndarray, pd.Series)):
        x = np.asarray(x)
    return np.argmin(x) / len(x) if len(x) > 0 else np.NaN