1.导入模块
>>> import pandas as pd
>>> pd.set_option('display.max_rows', 100,'display.max_columns', 1000,"display.max_colwidth",1000,'display.width',1000) #设置显示
2.导入数据
>>> air_quality_no2 = pd.read_csv(r"C:\Users\Administrator\Desktop\air_quality_no2_long.csv", parse_dates=True)
>>> air_quality_no2 = air_quality_no2[["date.utc", "location", "parameter", "value"]]
>>> air_quality_no2
date.utc location parameter value
0 2019-06-21 00:00:00+00:00 FR04014 no2 20.0
1 2019-06-20 23:00:00+00:00 FR04014 no2 21.8
2 2019-06-20 22:00:00+00:00 FR04014 no2 26.5
3 2019-06-20 21:00:00+00:00 FR04014 no2 24.9
4 2019-06-20 20:00:00+00:00 FR04014 no2 21.4
... ... ... ... ...
2063 2019-05-07 06:00:00+00:00 London Westminster no2 26.0
2064 2019-05-07 04:00:00+00:00 London Westminster no2 16.0
2065 2019-05-07 03:00:00+00:00 London Westminster no2 19.0
2066 2019-05-07 02:00:00+00:00 London Westminster no2 19.0
2067 2019-05-07 01:00:00+00:00 London Westminster no2 23.0
[2068 rows x 4 columns]
>>>
>>> air_quality_pm25 = pd.read_csv(r"C:\Users\Administrator\Desktop\air_quality_pm25_long.csv", parse_dates=True)
>>> air_quality_pm25 = air_quality_pm25[["date.utc", "location", "parameter", "value"]]
>>> air_quality_pm25
date.utc location parameter value
0 2019-06-18 06:00:00+00:00 BETR801 pm25 18.0
1 2019-06-17 08:00:00+00:00 BETR801 pm25 6.5
2 2019-06-17 07:00:00+00:00 BETR801 pm25 18.5
3 2019-06-17 06:00:00+00:00 BETR801 pm25 16.0
4 2019-06-17 05:00:00+00:00 BETR801 pm25 7.5
... ... ... ... ...
1105 2019-05-07 06:00:00+00:00 London Westminster pm25 9.0
1106 2019-05-07 04:00:00+00:00 London Westminster pm25 8.0
1107 2019-05-07 03:00:00+00:00 London Westminster pm25 8.0
1108 2019-05-07 02:00:00+00:00 London Westminster pm25 8.0
1109 2019-05-07 01:00:00+00:00 London Westminster pm25 8.0
[1110 rows x 4 columns]
3.合并数据，上下拼接
合并方式
>>> air_quality = pd.concat([air_quality_pm25, air_quality_no2], axis=0)
>>> air_quality
date.utc location parameter value
0 2019-06-18 06:00:00+00:00 BETR801 pm25 18.0
1 2019-06-17 08:00:00+00:00 BETR801 pm25 6.5
2 2019-06-17 07:00:00+00:00 BETR801 pm25 18.5
3 2019-06-17 06:00:00+00:00 BETR801 pm25 16.0
4 2019-06-17 05:00:00+00:00 BETR801 pm25 7.5
... ... ... ... ...
2063 2019-05-07 06:00:00+00:00 London Westminster no2 26.0
2064 2019-05-07 04:00:00+00:00 London Westminster no2 16.0
2065 2019-05-07 03:00:00+00:00 London Westminster no2 19.0
2066 2019-05-07 02:00:00+00:00 London Westminster no2 19.0
2067 2019-05-07 01:00:00+00:00 London Westminster no2 23.0
[3178 rows x 4 columns]
>>> air_quality = air_quality.sort_values("date.utc") #按时间排序
>>> air_quality
date.utc location parameter value
2067 2019-05-07 01:00:00+00:00 London Westminster no2 23.0
1003 2019-05-07 01:00:00+00:00 FR04014 no2 25.0
100 2019-05-07 01:00:00+00:00 BETR801 pm25 12.5
1098 2019-05-07 01:00:00+00:00 BETR801 no2 50.5
1109 2019-05-07 01:00:00+00:00 London Westminster pm25 8.0
... ... ... ... ...
2 2019-06-20 22:00:00+00:00 FR04014 no2 26.5
102 2019-06-20 23:00:00+00:00 London Westminster pm25 7.0
1 2019-06-20 23:00:00+00:00 FR04014 no2 21.8
101 2019-06-21 00:00:00+00:00 London Westminster pm25 7.0
0 2019-06-21 00:00:00+00:00 FR04014 no2 20.0
[3178 rows x 4 columns]
axis=0、axis=index，指的是遍历每个index、行号，即在纵向上遍历每列。axis=1、axis=columns，指的是遍历每个columns、列名，即在横向上遍历每行。
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