dataframe 取2列_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:

importpandas as pd

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

A B

0 03

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 R00705-01-11 0 0 2 5005-02-11 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 27005-09-11 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:

05-01-11005-02-11 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 1342Name: 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

R0052R0065R007 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 50]

[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 1 270]

[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:

defmean_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)printmean_riders_for_max_station(ridership_df)#结果:

(2342.6, 3239.9)