合并来自多个表的数据

import pandas as pd
air_quality_no2=pd.read_csv("data/air_quality_long.csv",parse_dates=True)
#parse_dates参数：将csv中的时间字符串转换成日期格式
air_quality_no2=air_quality_no2[["date.utc","location","parameter","value"]]
air_quality_no2.head()

	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

air_quality_pm25=pd.read_csv("data/air_quality_pm25_long.csv",parse_dates=True)
air_quality_pm25.head()

	city	country	date.utc	location	parameter	value	unit
0	Antwerpen	BE	2019-06-18 06:00:00+00:00	BETR801	pm25	18.0	µg/m³
1	Antwerpen	BE	2019-06-17 08:00:00+00:00	BETR801	pm25	6.5	µg/m³
2	Antwerpen	BE	2019-06-17 07:00:00+00:00	BETR801	pm25	18.5	µg/m³
3	Antwerpen	BE	2019-06-17 06:00:00+00:00	BETR801	pm25	16.0	µg/m³
4	Antwerpen	BE	2019-06-17 05:00:00+00:00	BETR801	pm25	7.5	µg/m³

1. concat()函数:

沿一个轴（行或列）执行多个表的串联操作
默认沿行进行拼接
concat（）的keys参数：提供了一个附加的（分层的）行索引
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air_quality=pd.concat([air_quality_pm25,air_quality_no2])
air_quality.head()

	city	country	date.utc	location	parameter	value	unit
0	Antwerpen	BE	2019-06-18 06:00:00+00:00	BETR801	pm25	18.0	µg/m³
1	Antwerpen	BE	2019-06-17 08:00:00+00:00	BETR801	pm25	6.5	µg/m³
2	Antwerpen	BE	2019-06-17 07:00:00+00:00	BETR801	pm25	18.5	µg/m³
3	Antwerpen	BE	2019-06-17 06:00:00+00:00	BETR801	pm25	16.0	µg/m³
4	Antwerpen	BE	2019-06-17 05:00:00+00:00	BETR801	pm25	7.5	µg/m³

print("Shape of pm25:",air_quality_pm25.shape)
print("Shape of no2:",air_quality_no2.shape)
print("shape of quality:",air_quality.shape)
#3178 = 1110 + 2068行。

Shape of pm25: (1110, 7)
Shape of no2: (5272, 4)
shape of quality: (6382, 7)

air_quality=air_quality.sort_values("date.utc")
air_quality

	city	country	date.utc	location	parameter	value	unit
3500	NaN	NaN	2019-04-09 01:00:00+00:00	FR04014	no2	24.4	NaN
176	NaN	NaN	2019-04-09 01:00:00+00:00	BETR801	pm25	76.0	NaN
3663	NaN	NaN	2019-04-09 01:00:00+00:00	BETR801	no2	22.5	NaN
5271	NaN	NaN	2019-04-09 02:00:00+00:00	London Westminster	no2	67.0	NaN
1824	NaN	NaN	2019-04-09 02:00:00+00:00	London Westminster	pm25	42.0	NaN
...	...	...	...	...	...	...	...
102	London	GB	2019-06-20 23:00:00+00:00	London Westminster	pm25	7.0	µg/m³
178	NaN	NaN	2019-06-20 23:00:00+00:00	London Westminster	pm25	7.0	NaN
177	NaN	NaN	2019-06-21 00:00:00+00:00	London Westminster	pm25	7.0	NaN
1825	NaN	NaN	2019-06-21 00:00:00+00:00	FR04014	no2	20.0	NaN
101	London	GB	2019-06-21 00:00:00+00:00	London Westminster	pm25	7.0	µg/m³

6382 rows × 7 columns

#concat的keys参数提供了一个方便的解决方案，添加了一个附加的（分层的）行索引
air_quality_=pd.concat([air_quality_pm25,air_quality_no2],keys=["PM25","NO2"])
air_quality_

		city	country	date.utc	location	parameter	value	unit
PM25	0	Antwerpen	BE	2019-06-18 06:00:00+00:00	BETR801	pm25	18.0	µg/m³
	1	Antwerpen	BE	2019-06-17 08:00:00+00:00	BETR801	pm25	6.5	µg/m³
	2	Antwerpen	BE	2019-06-17 07:00:00+00:00	BETR801	pm25	18.5	µg/m³
	3	Antwerpen	BE	2019-06-17 06:00:00+00:00	BETR801	pm25	16.0	µg/m³
	4	Antwerpen	BE	2019-06-17 05:00:00+00:00	BETR801	pm25	7.5	µg/m³
...	...	...	...	...	...	...	...	...
NO2	5267	NaN	NaN	2019-04-09 06:00:00+00:00	London Westminster	no2	41.0	NaN
	5268	NaN	NaN	2019-04-09 05:00:00+00:00	London Westminster	no2	41.0	NaN
	5269	NaN	NaN	2019-04-09 04:00:00+00:00	London Westminster	no2	41.0	NaN
	5270	NaN	NaN	2019-04-09 03:00:00+00:00	London Westminster	no2	67.0	NaN
	5271	NaN	NaN	2019-04-09 02:00:00+00:00	London Westminster	no2	67.0	NaN

6382 rows × 7 columns

2. reset_index（）：将任何级别的索引转换为列

air_quality.reset_index(level=0)

	index	city	country	date.utc	location	parameter	value	unit
0	3500	NaN	NaN	2019-04-09 01:00:00+00:00	FR04014	no2	24.4	NaN
1	176	NaN	NaN	2019-04-09 01:00:00+00:00	BETR801	pm25	76.0	NaN
2	3663	NaN	NaN	2019-04-09 01:00:00+00:00	BETR801	no2	22.5	NaN
3	5271	NaN	NaN	2019-04-09 02:00:00+00:00	London Westminster	no2	67.0	NaN
4	1824	NaN	NaN	2019-04-09 02:00:00+00:00	London Westminster	pm25	42.0	NaN
...	...	...	...	...	...	...	...	...
6377	102	London	GB	2019-06-20 23:00:00+00:00	London Westminster	pm25	7.0	µg/m³
6378	178	NaN	NaN	2019-06-20 23:00:00+00:00	London Westminster	pm25	7.0	NaN
6379	177	NaN	NaN	2019-06-21 00:00:00+00:00	London Westminster	pm25	7.0	NaN
6380	1825	NaN	NaN	2019-06-21 00:00:00+00:00	FR04014	no2	20.0	NaN
6381	101	London	GB	2019-06-21 00:00:00+00:00	London Westminster	pm25	7.0	µg/m³

6382 rows × 8 columns

merge():加入使用公共标识符表（自然连接）

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station_coord=pd.read_csv("data/air_quality_stations.csv")
station_coord.head()

	location	coordinates.latitude	coordinates.longitude
0	BELAL01	51.23619	4.38522
1	BELHB23	51.17030	4.34100
2	BELLD01	51.10998	5.00486
3	BELLD02	51.12038	5.02155
4	BELR833	51.32766	4.36226

air_quality.head()

	city	country	date.utc	location	parameter	value	unit
3500	NaN	NaN	2019-04-09 01:00:00+00:00	FR04014	no2	24.4	NaN
176	NaN	NaN	2019-04-09 01:00:00+00:00	BETR801	pm25	76.0	NaN
3663	NaN	NaN	2019-04-09 01:00:00+00:00	BETR801	no2	22.5	NaN
5271	NaN	NaN	2019-04-09 02:00:00+00:00	London Westminster	no2	67.0	NaN
1824	NaN	NaN	2019-04-09 02:00:00+00:00	London Westminster	pm25	42.0	NaN

air_quality=pd.merge(air_quality,station_coord,how='left',on='location')
air_quality.head()

	city	country	date.utc	location	parameter	value	unit	coordinates.latitude	coordinates.longitude
0	NaN	NaN	2019-04-09 01:00:00+00:00	FR04014	no2	24.4	NaN	48.83724	2.39390
1	NaN	NaN	2019-04-09 01:00:00+00:00	FR04014	no2	24.4	NaN	48.83722	2.39390
2	NaN	NaN	2019-04-09 01:00:00+00:00	BETR801	pm25	76.0	NaN	51.20966	4.43182
3	NaN	NaN	2019-04-09 01:00:00+00:00	BETR801	no2	22.5	NaN	51.20966	4.43182
4	NaN	NaN	2019-04-09 02:00:00+00:00	London Westminster	no2	67.0	NaN	51.49467	-0.13193

air_quality_parameters = pd.read_csv("data/air_quality_parameters.csv")
air_quality_parameters.head()

	id	description	name
0	bc	Black Carbon	BC
1	co	Carbon Monoxide	CO
2	no2	Nitrogen Dioxide	NO2
3	o3	Ozone	O3
4	pm10	Particulate matter less than 10 micrometers in...	PM10

air_quality=pd.merge(air_quality,air_quality_parameters,how='left',left_on="parameter",right_on="id")
air_quality.head()
#在left_on和right_on 参数都在这里（而不是仅仅使用on），使两个表之间的联系。

	city	country	date.utc	location	parameter	value	unit	coordinates.latitude	coordinates.longitude	id_x	description_x	name_x	id_y	description_y	name_y
0	NaN	NaN	2019-04-09 01:00:00+00:00	FR04014	no2	24.4	NaN	48.83724	2.39390	no2	Nitrogen Dioxide	NO2	no2	Nitrogen Dioxide	NO2
1	NaN	NaN	2019-04-09 01:00:00+00:00	FR04014	no2	24.4	NaN	48.83722	2.39390	no2	Nitrogen Dioxide	NO2	no2	Nitrogen Dioxide	NO2
2	NaN	NaN	2019-04-09 01:00:00+00:00	BETR801	pm25	76.0	NaN	51.20966	4.43182	pm25	Particulate matter less than 2.5 micrometers i...	PM2.5	pm25	Particulate matter less than 2.5 micrometers i...	PM2.5
3	NaN	NaN	2019-04-09 01:00:00+00:00	BETR801	no2	22.5	NaN	51.20966	4.43182	no2	Nitrogen Dioxide	NO2	no2	Nitrogen Dioxide	NO2
4	NaN	NaN	2019-04-09 02:00:00+00:00	London Westminster	no2	67.0	NaN	51.49467	-0.13193	no2	Nitrogen Dioxide	NO2	no2	Nitrogen Dioxide	NO2

小结：

使用该concat功能，可以将多个表按行连接到两列。
对于类似数据库的表合并/联接，请使用该merge 函数。

笔记——pandas学习3

合并来自多个表的数据

1. concat()函数:

2. reset_index（）：将任何级别的索引转换为列

merge():加入使用公共标识符表（自然连接）

小结：

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