The dataset contains daily airline information covering from flight information, carrier company, to taxing-in, taxing-out time, and generalized delay reason for 3 years, 2018, 2019 and 2020. We want to understand how the location(airport/city/state), carrier, time, flight performance, distance and some other features affect the delay/cancel status, duration and reasons. For example, when the passengers should expect a super delay based on the information they have obtained, such as the size of their departure airport, the carrier of their flights, whether their flights are in a special period of one year, whether any unexpected situation occurred, and the consequences of delayed departure time, such as shorter or longer flying time. Since our dataset includes the data from 2020, we also focus on the impact of COVID-19 on the conclusions to our interested questions based on the data from 2018 and 2019. Till now, we find the arriving delay has slight negative correlations with the flying time and the flying distance. In February and October, arriving delay due to security reason is more frequent.