Python之pandas表格合并

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

>>> 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。