--//创建一个信息表

CREATE TABLE user_student(id decimal(18,0) identity(1,1),st_name nvarchar(30),class nvarchar(10),score decimal(18,2))

--//插入测试数据============start===================

insert into user_student(st_name,class,score)

values('张三','甲','')

insert into user_student(st_name,class,score)

values('张四','甲','')

insert into user_student(st_name,class,score)

values('张五','甲','')

insert into user_student(st_name,class,score)

values('李三','乙','')

insert into user_student(st_name,class,score)

values('李四','乙','')

insert into user_student(st_name,class,score)

values('李五','乙','')

insert into user_student(st_name,class,score)

values('王三','丙','')

insert into user_student(st_name,class,score)

values('王四','丙','')

insert into user_student(st_name,class,score)

values('王五','丙','')

--//插入测试数据============end===================

select * from user_student

--//每个班级分数前两名的学生信息

SELECT ST_NAME,CLASS,SCORE

FROM (

SELECT Row_number() OVER(PARTITION BY CLASS ORDER BY SCORE DESC) AS NUM,*

FROM user_student

) AS T

WHERE NUM<=2

表内数据：aaarticlea/png;base64,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" 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