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+import pandas as pd%0A%0A# Subway ridership for 5 stations on 10 different days%0Aridership_df = pd.DataFrame(%0A    data=%5B%5B0, 0, 2, 5, 0%5D,%0A          %5B1478, 3877, 3674, 2328, 2539%5D,%0A          %5B1613, 4088, 3991, 6461, 2691%5D,%0A          %5B1560, 3392, 3826, 4787, 2613%5D,%0A          %5B1608, 4802, 3932, 4477, 2705%5D,%0A          %5B1576, 3933, 3909, 4979, 2685%5D,%0A          %5B95, 229, 255, 496, 201%5D,%0A          %5B2, 0, 1, 27, 0%5D,%0A          %5B1438, 3785, 3589, 4174, 2215%5D,%0A          %5B1342, 4043, 4009, 4665, 3033%5D%5D,%0A    index=%5B'05-01-11', '05-02-11', '05-03-11', '05-04-11', '05-05-11',%0A           '05-06-11', '05-07-11', '05-08-11', '05-09-11', '05-10-11'%5D,%0A    columns=%5B'R003', 'R004', 'R005', 'R006', 'R007'%5D%0A)%0A%0A%0Adef mean_max_riders(data):%0A    '''%0A    find the station with the maximum riders on the%0A    first day, return the mean riders per day for that station and%0A    the mean ridership overall for comparsion.%0A    '''%0A    max_station = data.iloc%5B0%5D.argmax()%0A    mean_for_max = data%5Bmax_station%5D.mean()%0A    overall_mean = data.values.mean()%0A%0A    return overall_mean, mean_for_max%0A%0Aprint mean_max_riders(ridership_df)%0A

Create rider_mean.py