用MySQL计算中位数最简单(希望不会太慢)的方法是什么?我已经使用AVG(x)来寻找平均值，但我很难找到一个简单的方法来计算中位数。现在，我将所有的行返回到PHP，进行排序，然后选择中间的行，但是肯定有一些简单的方法可以在一个MySQL查询中完成它。
示例数据:
id | val
--------
1 4
2 7
3 2
4 2
5 9
6 8
7 3
对val排序得到2 2 3 4 7 8 9，因此中位数应该是4，而SELECT AVG(val) == 5。
当前回答
归档完美中位数的单个查询:
SELECT
COUNT(*) as total_rows,
IF(count(*)%2 = 1, CAST(SUBSTRING_INDEX(SUBSTRING_INDEX( GROUP_CONCAT(val ORDER BY val SEPARATOR ','), ',', 50/100 * COUNT(*)), ',', -1) AS DECIMAL), ROUND((CAST(SUBSTRING_INDEX(SUBSTRING_INDEX( GROUP_CONCAT(val ORDER BY val SEPARATOR ','), ',', 50/100 * COUNT(*) + 1), ',', -1) AS DECIMAL) + CAST(SUBSTRING_INDEX(SUBSTRING_INDEX( GROUP_CONCAT(val ORDER BY val SEPARATOR ','), ',', 50/100 * COUNT(*)), ',', -1) AS DECIMAL)) / 2)) as median,
AVG(val) as average
FROM
data
其他回答
安装和使用本mysql统计函数:http://www.xarg.org/2012/07/statistical-functions-in-mysql/
之后，计算中值就很简单了:
SELECT median(val) FROM data;
让我们创建一个名为numbers的示例表
这个答案是针对mysql数据库的
在postgres Sql中，它简单地使用per_cont函数
创建表数字( num INT, 频率整数 ）;
在数字表中插入值
插入数字 (7) 0 (1, 1), (2、3), (1) 3 (9,1), (1, 1), (2、3), (1) 3 (9,1);
——select * from numbers
作为递归num_frequency (num,frequency, i) （ 选择num,频率,1 从数字 UNION ALL 选择num,频率,i + 1 从num_frequency num_frequency的地方。I < num_frequency.frequency ）
select * (max(当numbers=lower_limit时，则num else null end)/2 +max(当数字=upper_limit时，则num else null end)/2)作为中位数 从( select *, total_number % 2, 情况下 当total_number%2=0时，total_number/2 Else (total_number+1)/2 end as lower_limit， 情况下 当total_number%2=0时，total_number/2+1 其他(total_number + 1) / 2 结束为upper_limit
从( Select *，max(numbers) over() as total_number from ( Select num,row_number() over(按num排序) 作为num_frequency中的数字 b) b) b)
你可以使用这里的用户定义函数。
基于@bob的回答，这将查询泛化为能够返回多个中位数，并按某些标准分组。
想想，例如，一个车场二手车的中位数销售价格，按年-月分组。
SELECT
period,
AVG(middle_values) AS 'median'
FROM (
SELECT t1.sale_price AS 'middle_values', t1.row_num, t1.period, t2.count
FROM (
SELECT
@last_period:=@period AS 'last_period',
@period:=DATE_FORMAT(sale_date, '%Y-%m') AS 'period',
IF (@period<>@last_period, @row:=1, @row:=@row+1) as `row_num`,
x.sale_price
FROM listings AS x, (SELECT @row:=0) AS r
WHERE 1
-- where criteria goes here
ORDER BY DATE_FORMAT(sale_date, '%Y%m'), x.sale_price
) AS t1
LEFT JOIN (
SELECT COUNT(*) as 'count', DATE_FORMAT(sale_date, '%Y-%m') AS 'period'
FROM listings x
WHERE 1
-- same where criteria goes here
GROUP BY DATE_FORMAT(sale_date, '%Y%m')
) AS t2
ON t1.period = t2.period
) AS t3
WHERE
row_num >= (count/2)
AND row_num <= ((count/2) + 1)
GROUP BY t3.period
ORDER BY t3.period;
MySQL文档中这一页的注释有以下建议:
-- (mostly) High Performance scaling MEDIAN function per group
-- Median defined in http://en.wikipedia.org/wiki/Median
--
-- by Peter Hlavac
-- 06.11.2008
--
-- Example Table:
DROP table if exists table_median;
CREATE TABLE table_median (id INTEGER(11),val INTEGER(11));
COMMIT;
INSERT INTO table_median (id, val) VALUES
(1, 7), (1, 4), (1, 5), (1, 1), (1, 8), (1, 3), (1, 6),
(2, 4),
(3, 5), (3, 2),
(4, 5), (4, 12), (4, 1), (4, 7);
-- Calculating the MEDIAN
SELECT @a := 0;
SELECT
id,
AVG(val) AS MEDIAN
FROM (
SELECT
id,
val
FROM (
SELECT
-- Create an index n for every id
@a := (@a + 1) mod o.c AS shifted_n,
IF(@a mod o.c=0, o.c, @a) AS n,
o.id,
o.val,
-- the number of elements for every id
o.c
FROM (
SELECT
t_o.id,
val,
c
FROM
table_median t_o INNER JOIN
(SELECT
id,
COUNT(1) AS c
FROM
table_median
GROUP BY
id
) t2
ON (t2.id = t_o.id)
ORDER BY
t_o.id,val
) o
) a
WHERE
IF(
-- if there is an even number of elements
-- take the lower and the upper median
-- and use AVG(lower,upper)
c MOD 2 = 0,
n = c DIV 2 OR n = (c DIV 2)+1,
-- if its an odd number of elements
-- take the first if its only one element
-- or take the one in the middle
IF(
c = 1,
n = 1,
n = c DIV 2 + 1
)
)
) a
GROUP BY
id;
-- Explanation:
-- The Statement creates a helper table like
--
-- n id val count
-- ----------------
-- 1, 1, 1, 7
-- 2, 1, 3, 7
-- 3, 1, 4, 7
-- 4, 1, 5, 7
-- 5, 1, 6, 7
-- 6, 1, 7, 7
-- 7, 1, 8, 7
--
-- 1, 2, 4, 1
-- 1, 3, 2, 2
-- 2, 3, 5, 2
--
-- 1, 4, 1, 4
-- 2, 4, 5, 4
-- 3, 4, 7, 4
-- 4, 4, 12, 4
-- from there we can select the n-th element on the position: count div 2 + 1
