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']
)
print ridership_df
R003 R004 R005 R006 R007
05-01-11 0 0 2 5 0
05-02-11 1478 3877 3674 2328 2539
05-03-11 1613 4088 3991 6461 2691
05-04-11 1560 3392 3826 4787 2613
05-05-11 1608 4802 3932 4477 2705
05-06-11 1576 3933 3909 4979 2685
05-07-11 95 229 255 496 201
05-08-11 2 0 1 27 0
05-09-11 1438 3785 3589 4174 2215
05-10-11 1342 4043 4009 4665 3033
print ridership_df.iloc[0]
print ridership_df.loc['05-05-11']
print ridership_df['R003']
print ridership_df.iloc[1, 3]
R003 0
R004 0
R005 2
R006 5
R007 0
Name: 05-01-11, dtype: int64
R003 1608
R004 4802
R005 3932
R006 4477
R007 2705
Name: 05-05-11, dtype: int64
05-01-11 0
05-02-11 1478
05-03-11 1613
05-04-11 1560
05-05-11 1608
05-06-11 1576
05-07-11 95
05-08-11 2
05-09-11 1438
05-10-11 1342
Name: R003, dtype: int64
2328
print ridership_df.iloc[1:4]
R003 R004 R005 R006 R007
05-02-11 1478 3877 3674 2328 2539
05-03-11 1613 4088 3991 6461 2691
05-04-11 1560 3392 3826 4787 2613
df_1 = pd.DataFrame({'A': [0, 1, 2], 'B': [3, 4, 5]})
print df_1
df_2 = pd.DataFrame([[0, 1, 2], [3, 4, 5]], columns=['A', 'B', 'C'])
print df_2
A B
0 0 3
1 1 4
2 2 5
A B C
0 0 1 2
1 3 4 5
df = pd.DataFrame({'A': [0, 1, 2], 'B': [3, 4, 5]})
print df.sum()
print df.sum(axis=1)
print df.values.sum()
df = pd.DataFrame({'A': [0, 1, 2], 'B': [3, 4, 5]})
print df.sum()
print df.sum(axis=1)
print df.values.sum()
A 3
B 12
dtype: int64
0 3
1 5
2 7
dtype: int64
15