pandas DataFrame(2)-行列索引及值的获取

pandas DataFrame是二维的,所以,它既有列索引,又有行索引

上一篇里只介绍了列索引:

import pandas as pd

df = pd.DataFrame({'A': [0, 1, 2], 'B': [3, 4, 5]})
print df

# 结果:
   A  B
0  0  3
1  1  4
2  2  5

行索引自动生成了 0,1,2

如果要自己指定行索引和列索引,可以使用 index 和 column 参数:

这个数据是5个车站10天内的客流数据:

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']
)

data 参数为一个numpy二维数组,  index 参数为行索引, column 参数为列索引

生成的数据以表格形式显示:

          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

下面说下如何获取DataFrame里的值:

1.获取某一列: 直接 ['key']

print(ridership_df['R003'])

# 结果:
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

2.获取某一行:  .loc['key']

print(ridership_df.loc['05-01-11'])
# 或者
print(ridership_df.iloc[0])

# 结果:
R003    0
R004    0
R005    2
R006    5
R007    0
Name: 05-01-11, dtype: int64

3.获取某一行某一列的某个值:

print(ridership_df.loc['05-05-11','R003'])
# 或者
print(ridership_df.iloc[4,0])

# 结果:
1608

4.获取原始的numpy二维数组:

print(ridership_df.values)

# 结果:
[[   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]]

*注意在这过程中,数据格式如果不一致,会发生转换.

一个综合栗子:

从 ridership_df 找出第一天里客流量最多的车站,然后返回这个车站的日平均客流,以及返回所有车站的平均日客流,作为对比:

def mean_riders_for_max_station(ridership):
    max_index = ridership.iloc[0].argmax()
    mean_for_max = ridership[max_index].mean()
    overall_mean = ridership.values.mean()
    return (overall_mean, mean_for_max)

print mean_riders_for_max_station(ridership_df)

# 结果:
(2342.6, 3239.9)