06. Accessing Elements of a DataFrame

import pandas as pd

# Subway ridership for 5 stations on 10 different days
ridership_df = pd.DataFrame(
    data=[[   0,    0,    2,    5,    0],
          [1478, 3877, 3674, 2328, 2539],
          [1613, 4088, 3991, 6461, 2691],
          [1560, 3392, 3826, 4787, 2613],
          [1608, 4802, 3932, 4477, 2705],
          [1576, 3933, 3909, 4979, 2685],
          [  95,  229,  255,  496,  201],
          [   2,    0,    1,   27,    0],
          [1438, 3785, 3589, 4174, 2215],
          [1342, 4043, 4009, 4665, 3033]],
    index=['05-01-11', '05-02-11', '05-03-11', '05-04-11', '05-05-11',
           '05-06-11', '05-07-11', '05-08-11', '05-09-11', '05-10-11'],
    columns=['R003', 'R004', 'R005', 'R006', 'R007']
)

# Change False to True for each block of code to see what it does

# DataFrame creation
if False:
    # You can create a DataFrame out of a dictionary mapping column names to values
    df_1 = pd.DataFrame({'A': [0, 1, 2], 'B': [3, 4, 5]})
    print df_1

    # You can also use a list of lists or a 2D NumPy array
    df_2 = pd.DataFrame([[0, 1, 2], [3, 4, 5]], columns=['A', 'B', 'C'])
    print df_2
   

# Accessing elements
if False:
    print ridership_df.iloc[0]
    print ridership_df.loc['05-05-11']
    print ridership_df['R003']
    print ridership_df.iloc[1, 3]
    
# Accessing multiple rows
if False:
    print ridership_df.iloc[1:4]
    
# Accessing multiple columns
if False:
    print ridership_df[['R003', 'R005']]
    
# Pandas axis
if False:
    df = pd.DataFrame({'A': [0, 1, 2], 'B': [3, 4, 5]})
    print df.sum()
    print df.sum(axis=1)
    print df.values.sum()
    
def mean_riders_for_max_station(ridership):
    '''
    Fill in this function to find the station with the maximum riders on the
    first day, then return the mean riders per day for that station. Also
    return the mean ridership overall for comparsion.
    
    This is the same as a previous exercise, but this time the
    input is a Pandas DataFrame rather than a 2D NumPy array.
    '''
    overall_mean = None # Replace this with your code
    mean_for_max = None # Replace this with your code
    
    return (overall_mean, mean_for_max)