code: def airline_delay(data_set='airline_delay', num_train=700000, num_test=100000, seed=default_seed):
      
  
      if not data_available(data_set):
          download_data(data_set)
  
      dir_path = os.path.join(data_path, data_set)
      filename = os.path.join(dir_path, 'filtered_data.pickle')
  
      # 1. Load the dataset
      import pandas as pd
      data = pd.read_pickle(filename)
  
      # WARNING: removing year
      data.pop('Year')
  
      # Get data matrices
      Yall = data.pop('ArrDelay').values[:,None]
      Xall = data.values
  
      # Subset the data (memory!!)
      all_data = num_train+num_test
      Xall = Xall[:all_data]
      Yall = Yall[:all_data]
  
      # Get testing points
      np.random.seed(seed=seed)
      N_shuffled = permute(Yall.shape[0])
      train, test = N_shuffled[num_test:], N_shuffled[:num_test]
      X, Y = Xall[train], Yall[train]
      Xtest, Ytest = Xall[test], Yall[test]
      covariates =  ['month', 'day of month', 'day of week', 'departure time', 'arrival time', 'air time', 'distance to travel', 'age of aircraft / years']
      response = ['delay']
      return data_details_return({'X': X, 'Y': Y, 'Xtest': Xtest, 'Ytest': Ytest, 'seed' : seed, 'info': "Airline delay data used for demonstrating Gaussian processes for big data.", 'covariates': covariates, 'response': response}, data_set)
docs: Airline delay data used in Gaussian Processes for Big Data by Hensman, Fusi and Lawrence