How to use the quantlib.util.converter.df_to_zero_curve function in QuantLib

settings = Settings()
    settings.evaluation_date = DtSettlement

    calendar = TARGET()

    if df_rates is None:
        df_tmp = DataFrame.filter(df_option, items=['dtExpiry', 'IR', 'IDIV'])
        grouped = df_tmp.groupby('dtExpiry')
        df_rates = grouped.agg(lambda x: x[0])

    # convert data frame (date/value) into zero curve
    # expect the index to be a date, and 1 column of values

    risk_free_ts = df_to_zero_curve(df_rates['R'], dtTrade)
    dividend_ts = df_to_zero_curve(df_rates['D'], dtTrade)

    # back out the spot from any forward
    iRate = df_option['R'][0]
    iDiv = df_option['D'][0]
    TTM = df_option['T'][0]
    Fwd = df_option['F'][0]
    spot = SimpleQuote(Fwd * np.exp(-(iRate - iDiv) * TTM))
    print('Spot: %f risk-free rate: %f div. yield: %f' % \
          (spot.value, iRate, iDiv))

    # loop through rows in option data frame, construct
    # helpers for bid/ask

    oneDay = datetime.timedelta(days=1)
    dtExpiry = [dtTrade + int(t * 365) * oneDay for t in df_option['T']]
    df_option['dtExpiry'] = dtExpiry

DtSettlement = pydate_to_qldate(dtTrade)

    settings = Settings()
    settings.evaluation_date = DtSettlement

    calendar = TARGET()

    if df_rates is None:
        df_tmp = DataFrame.filter(df_option, items=['dtExpiry', 'IR', 'IDIV'])
        grouped = df_tmp.groupby('dtExpiry')
        df_rates = grouped.agg(lambda x: x[0])

    # convert data frame (date/value) into zero curve
    # expect the index to be a date, and 1 column of values

    risk_free_ts = df_to_zero_curve(df_rates['R'], dtTrade)
    dividend_ts = df_to_zero_curve(df_rates['D'], dtTrade)

    # back out the spot from any forward
    iRate = df_option['R'][0]
    iDiv = df_option['D'][0]
    TTM = df_option['T'][0]
    Fwd = df_option['F'][0]
    spot = SimpleQuote(Fwd * np.exp(-(iRate - iDiv) * TTM))
    print('Spot: %f risk-free rate: %f div. yield: %f' % \
          (spot.value, iRate, iDiv))

    # loop through rows in option data frame, construct
    # helpers for bid/ask

    oneDay = datetime.timedelta(days=1)
    dtExpiry = [dtTrade + int(t * 365) * oneDay for t in df_option['T']]

def bates_calibration(df_option, dtTrade=None, df_rates=None, ival=None):

    # array of option helpers
    hh = heston_helpers(df_option, dtTrade, df_rates, ival)
    options = hh['options']
    spot = hh['spot']

    risk_free_ts = df_to_zero_curve(df_rates['R'], dtTrade)
    dividend_ts = df_to_zero_curve(df_rates['D'], dtTrade)

    v0 = .02

    if ival is None:
        ival = {'v0': v0, 'kappa': 3.7, 'theta': v0,
        'sigma': 1.0, 'rho': -.6, 'lambda': .1,
        'nu': -.5, 'delta': 0.3}

    process = BatesProcess(
        risk_free_ts, dividend_ts, spot, ival['v0'], ival['kappa'],
         ival['theta'], ival['sigma'], ival['rho'],
         ival['lambda'], ival['nu'], ival['delta'])

    model = BatesModel(process)
    engine = BatesEngine(model, 64)

def heston_calibration(df_option, dtTrade=None, df_rates=None, ival=None):
    """
    calibrate heston model
    """

    # array of option helpers
    print df_option, df_rates, ival
    hh = heston_helpers(df_option, dtTrade, df_rates, ival)
    options = hh['options']
    spot = hh['spot']

    risk_free_ts = df_to_zero_curve(df_rates['R'], dtTrade)
    dividend_ts = df_to_zero_curve(df_rates['D'], dtTrade)

    if ival is None:
        ival = {'v0': 0.1, 'kappa': 1.0, 'theta': 0.1,
        'sigma': 0.5, 'rho': -.5}

    process = HestonProcess(
        risk_free_ts, dividend_ts, spot, ival['v0'], ival['kappa'],
         ival['theta'], ival['sigma'], ival['rho'])

    model = HestonModel(process)
    engine = AnalyticHestonEngine(model, 64)

    for option in options:
        option.set_pricing_engine(engine)

    om = LevenbergMarquardt(1e-8, 1e-8, 1e-8)

def heston_calibration(df_option, dtTrade=None, df_rates=None, ival=None):
    """
    calibrate heston model
    """

    # array of option helpers
    print df_option, df_rates, ival
    hh = heston_helpers(df_option, dtTrade, df_rates, ival)
    options = hh['options']
    spot = hh['spot']

    risk_free_ts = df_to_zero_curve(df_rates['R'], dtTrade)
    dividend_ts = df_to_zero_curve(df_rates['D'], dtTrade)

    if ival is None:
        ival = {'v0': 0.1, 'kappa': 1.0, 'theta': 0.1,
        'sigma': 0.5, 'rho': -.5}

    process = HestonProcess(
        risk_free_ts, dividend_ts, spot, ival['v0'], ival['kappa'],
         ival['theta'], ival['sigma'], ival['rho'])

    model = HestonModel(process)
    engine = AnalyticHestonEngine(model, 64)

    for option in options:
        option.set_pricing_engine(engine)

def batesdetjump_calibration(df_option, dtTrade=None,
                             df_rates=None, ival=None):

    # array of option helpers
    hh = heston_helpers(df_option, dtTrade, df_rates, ival)
    options = hh['options']
    spot = hh['spot']

    risk_free_ts = df_to_zero_curve(df_rates['R'], dtTrade)
    dividend_ts = df_to_zero_curve(df_rates['D'], dtTrade)

    v0 = .02

    if ival is None:
        ival = {'v0': v0, 'kappa': 3.7, 'theta': v0,
        'sigma': 1.0, 'rho': -.6, 'lambda': .1,
        'nu': -.5, 'delta': 0.3}

    process = BatesProcess(
        risk_free_ts, dividend_ts, spot, ival['v0'], ival['kappa'],
         ival['theta'], ival['sigma'], ival['rho'],
         ival['lambda'], ival['nu'], ival['delta'])

    model = BatesDetJumpModel(process)
    engine = BatesDetJumpEngine(model, 64)

def heston_pricer(trade_date, options, params, rates, spot):
    """
    Price a list of European options with heston model.

    """

    spot = SimpleQuote(spot)

    risk_free_ts = df_to_zero_curve(rates[nm.INTEREST_RATE], trade_date)
    dividend_ts = df_to_zero_curve(rates[nm.DIVIDEND_YIELD], trade_date)

    process = HestonProcess(risk_free_ts, dividend_ts, spot, **params)

    model = HestonModel(process)
    engine = AnalyticHestonEngine(model, 64)

    settlement_date = pydate_to_qldate(trade_date)

    settings = Settings()
    settings.evaluation_date = settlement_date

    modeled_values = np.zeros(len(options))

    for index, row in options.T.iteritems():