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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 _golden_sect_DataFrame(params, VL, VH, func):
 """ Vectorized golden section search for finding MPP from a dataframe timeseries. Parameters params : dict Dictionary containing scalars or arrays of inputs to the function to be optimized. Each row should represent an independent optimization. VL: float Lower bound of the optimization VH: float Upper bound of the optimization func: function Function to be optimized must be in the form f(array-like, x) Returns ------- func(df,'V1') : DataFrame function evaluated at the optimal point df['V1']: Dataframe Dataframe of optimal points Notes ----- This function will find the MAXIMUM of a function """


    df = params
    df['VH'] = VH
    df['VL'] = VL

    errflag = True
    iterations = 0

    while errflag:

        phi = (np.sqrt(5)-1)/2*(df['VH']-df['VL'])
        df['V1'] = df['VL'] + phi
        df['V2'] = df['VH'] - phi

        df['f1'] = func(df, 'V1')
        df['f2'] = func(df, 'V2')
        df['SW_Flag'] = df['f1'] > df['f2']

        df['VL'] = df['V2']*df['SW_Flag'] + df['VL']*(~df['SW_Flag'])
        df['VH'] = df['V1']*~df['SW_Flag'] + df['VH']*(df['SW_Flag'])

        err = df['V1'] - df['V2']
        try:
            errflag = (abs(err) > .01).any()
        except ValueError:
            errflag = (abs(err) > .01)

        iterations += 1

        if iterations > 50:
            raise Exception("EXCEPTION:iterations exceeded maximum (50)")

    return func(df, 'V1'), df['V1']