python pandas逐列计算平均值

Question

在pandas 0.18.1中，python 2.7.6：

想象一下，我们有下表：

ID,FROM_YEAR,FROM_MONTH,YEARMONTH,AREA,AREA2
1,2015,1,201501,200,100
1,2015,2,201502,200,100
1,2015,3,201503,200,100
1,2015,4,201504,200,100
1,2015,5,201505,200,100
1,2015,6,201506,200,100
1,2015,7,201507,200,100
1,2015,8,201508,200,100
1,2015,9,201509,200,100
1,2015,10,201510,200,100
1,2015,11,201511,200,100
1,2015,12,201512,200,100
1,2016,1,201601,100,200
1,2016,2,201602,100,200
1,2016,3,201603,100,200
1,2016,4,201604,100,200
1,2016,5,201605,100,200
1,2016,6,201606,100,200
1,2016,7,201607,100,200
1,2016,8,201608,100,200
1,2016,9,201609,100,200
1,2016,10,201610,100,200
1,2016,11,201611,100,200
1,2016,12,201612,100,200

有没有什么办法可以和python pandas中的以下MySQL查询做同样的事情（合并函数可能有用，但有没有办法避免在python pandas中进行昂贵的合并/连接）？

SELECT 
ID,
FROM_YEAR,
'A' AS TYPE,
AVG(AREA) AS AREA,
AVG(AREA2) AS AREA2
 FROM table GROUP BY ID,FROM_YEAR

UNION ALL 

SELECT 
ID,
FROM_YEAR,
'B' AS TYPE,
AVG(AREA) AS AREA,
AVG(AREA2) AS AREA2
 FROM table GROUP BY ID,FROM_YEAR;

此处的目标是按以下格式获取AREA和AREA2列的日历年平均值：

ID,FROM_YEAR,TYPE,AREA,AREA2
1,2015,A,200,100
1,2016,A,100,200
1,2015,B,200,100
1,2016,B,100,200

任何一位大师能开导吗？

=================================一个扩展的问题=========== ==

谢谢你的回答！我刚刚在一个尾随的12个案例中遇到另一个问题：

期望的输出：

ID,FROM_YEAR,FROM_MONTH,YEARMONTH,AREA,AREA2
1,2015,1,201501,NULL,NULL
1,2015,2,201502,NULL,NULL
1,2015,3,201503,NULL,NULL
1,2015,4,201504,NULL,NULL
1,2015,5,201505,NULL,NULL
1,2015,6,201506,NULL,NULL
1,2015,7,201507,NULL,NULL
1,2015,8,201508,NULL,NULL
1,2015,9,201509,NULL,NULL
1,2015,10,201510,NULL,NULL
1,2015,11,201511,NULL,NULL
1,2015,12,201512,200,100

以下代码

agg=df.groupby(['ID','FROM_YEAR'])[['AREA','AREA2']].rolling(window=12).mean()

只会生成此结果，其中缺少FROM_MONTH和YEARMONTH。

ID,FROM_YEAR,AREA,AREA2
1,2015,NULL,NULL
1,2015,NULL,NULL
1,2015,NULL,NULL
1,2015,NULL,NULL
1,2015,NULL,NULL
1,2015,NULL,NULL
1,2015,NULL,NULL
1,2015,NULL,NULL
1,2015,NULL,NULL
1,2015,NULL,NULL
1,2015,NULL,NULL
1,2015,200,100

有人能开导吗？谢谢！

Answer 1

您可以在此使用pandas.concat，它只涉及一个聚合，并且不会调用merge/join进程：

agg = df.groupby(['ID', 'FROM_YEAR'], as_index=False)[["AREA", "AREA2"]].mean()

pd.concat([agg.assign(TYPE = t) for t in ["A", "B"]], ignore_index=True)