我有一张这样的桌子:
| prodID | date | perm
---------------------------------
|200 |8/7/2011 | 81.742
|200 |8/7/2011 | 81.644
|200 |8/7/2011 | 81.302
|200 |8/7/2011 | 81.057
|201 |8/7/2011 | 80.932
|201 |8/7/2011 | 80.839
|201 |8/7/2011 | 80.622
|201 |8/7/2011 | 80.557
|201 |8/7/2011 | 80.541
(除了更大一点)发生的情况细分:我想取前 10 个值(和后 10 个值)的平均值,其中 prodid = somevalue 在这种情况下为 200。
代码:
declare @myid int
set @myid = 200
;with high as --top ten average
(
select prodid, CONVERT(CHAR(10), DATEADD(DAY, AVG(DATEDIFF(DAY, 0, CONVERT
(SMALLDATETIME, [date]))), 0), 101) as date, max(perm)as max_perm, avg(perm)
as
high_perm from
( select prodid, date, perm,
row_number() over(partition by date order by perm desc) as nt
from live_pilot_plant
where prodid = @myid) as T
where nt <= 10
group by prodid
),
low as -- bottom ten average
(
select prodid, CONVERT(CHAR(10), DATEADD(DAY, AVG(DATEDIFF(DAY, 0, CONVERT
(SMALLDATETIME, [date]))), 0),101) as date, min(perm) as min_perm, avg(perm)
as low_perm from
( select prodid, date, perm,
row_number() over(partition by date order by perm asc) as nt
from live_pilot_plant
where prodid = @myid) as T
where nt <= 10
group by prodid
)
select l.prodid, l.date, l.low_perm as low_avg, m.high_perm as high_avg,
(m.high_perm - l.low_perm) as delta
from low l
left outer join high m
on l.prodid = m.prodid
这会产生这样的东西:
| prodID | date | low_avg | high_avg | delta |
| 200 | 08/07/2011 | 68.752 | 79.1976 | 10.444 |
这些数字不准确——
这一切都很好而且花花公子 - 除了不是很通用。我的意思是有很多 prodID,而根据 prodID 来做一个太慢了。如何根据日期获取 low_avg 和 high_avg(按 prodID 分组)
像这样的东西:
| date | prodID | low_avg | high_avg | delta |
| 08/07/2011 | 200 | 60 | 80 | 20 |
| 08/07/2011 | 201 | 70 | 100 | 100 |
注意:您可能已经注意到日期前的疯狂转换。原因是某些 prodID 重叠日期,即。在 2011 年 8 月 7 日和 2011 年 8 月 8 日为 200,我需要平均日期(这是一个 varchar)。因此,如果有 100 行是 8/7/2011,然后是 9 行是 8/8/2011,最终查询将产生日期为 /8/7/2011