we can convert row into columns using pivot function
let us consider the following data as sample data
now create a table as emp_jobs with this data
![](data:image/png;base64,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)
using pivot function
we are converting rows into columns
![](data:image/png;base64,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)
the output is in the form
but while using pivot function aggregate function should be used
a1.deptno,
a1.ename AS PRESIDENT,
a2.ename AS MANAGER,
a3.ename AS CLERK
from
emp_jobs a1,
emp_jobs a2,
emp_jobs a3
where
a1.deptno=a2.deptno
and a2.deptno=a3.deptno
and a3.deptno=a1.deptno
and a1.job = 'PRESIDENT'
and a2.job = 'MANAGER'
and a3.job ='CLERK';
let us consider the following data as sample data
DEPTNO | ENAME | JOB |
10 | ALLEN | ANALYST |
10 | JONES | CLERK |
10 | FORD | MANAGER |
10 | ABCD | PRESIDENT |
10 | CLARK | SALESMAN |
20 | MILLER | ANALYST |
20 | SMITH | CLERK |
20 | WARD | MANAGER |
20 | efgh | PRESIDENT |
20 | MARTIN | SALESMAN |
30 | SCOTT | ANALYST |
30 | TURNER | CLERK |
30 | ADAMS | MANAGER |
30 | BLAKE | PRESIDENT |
30 | KING | SALESMAN |
now create a table as emp_jobs with this data
using pivot function
we are converting rows into columns
select * from emp_jobs pivot( min(ename) for job in ('PRESIDENT','MANAGER','CLERK','ANALYST','SALESMAN'));
the output is in the form
DEPTNO | PRESIDENT' | MANAGER' | CLERK' | ANALYST' | SALESMAN' |
20 | efgh | WARD | SMITH | MILLER | MARTIN |
10 | ABCD | FORD | JONES | ALLEN | CLARK |
30 | BLAKE | ADAMS | TURNER | SCOTT | KING |
but while using pivot function aggregate function should be used
we can also achieve this conversion without using pivot function using this sql query
selecta1.deptno,
a1.ename AS PRESIDENT,
a2.ename AS MANAGER,
a3.ename AS CLERK
from
emp_jobs a1,
emp_jobs a2,
emp_jobs a3
where
a1.deptno=a2.deptno
and a2.deptno=a3.deptno
and a3.deptno=a1.deptno
and a1.job = 'PRESIDENT'
and a2.job = 'MANAGER'
and a3.job ='CLERK';
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