For the weighted-average assumptions used to determine benefit obligation at September 30, what is the average discount rate across 2017, 2018 and 2019?
{"Weighted-average assumptions used to determine benefit obligation at September 30: -- Discount rate": {"2019": 2.5, "2018": 3.6, "2017": 3.3}, "Weighted-average assumptions used to determine benefit obligation at September 30: -- Rate of compensation increase": {"2019": 3.1, "2018": 3.3, "2017": 3.2}, "Weighted-average assumptions used to determine net periodic benefit cost for the years ended September 30: -- Discount rate": {"2019": 3.6, "2018": 3.3, "2017": 3.0}, "Weighted-average assumptions used to determine net periodic benefit cost for the years ended September 30: -- Expected return on plan assets": {"2019": 5.7, "2018": 6.8, "2017": 6.8}, "Weighted-average assumptions used to determine net periodic benefit cost for the years ended September 30: -- Rate of compensation increase": {"2019": 3.3, "2018": 3.2, "2017": 3.1}}
discount_rate_2019 = df["Weighted-average assumptions used to determine benefit obligation at September 30: -- Discount rate"]["2019"]
discount_rate_2018 = df["Weighted-average assumptions used to determine benefit obligation at September 30: -- Discount rate"]["2018"]
discount_rate_2017 = df["Weighted-average assumptions used to determine benefit obligation at September 30: -- Discount rate"]["2017"]
net_periodic_benefit_cost_discount_rate = df["Weighted-average assumptions used to determine net periodic benefit cost for the years ended September 30: -- Discount rate"]["2017"]
answer = ( discount_rate_2019 + discount_rate_2018 + discount_rate_2017 ) / net_periodic_benefit_cost_discount_rate