Reputation: 839
We have a set of values which we use to populate a bar chart. For this application, we will always need 5 years of data, we will always need 5 rows of data, even if the values are NULL.
See this query. Assume that the DATE column goes from 2017, 2016, 2015.........even those we may have no data for 2014 & 2013, I will need to return a 2014 & 2013 for, with a NULL as the other column.....
SELECT period_date, actual_eps
FROM (SELECT LAST_DAY(TO_DATE(TO_CHAR(period_date),'YYYYMM')) period_date, actual_eps
FROM period_data
WHERE ticker = 'ADRO'
AND period_type = 'A'
AND actual_eps IS NOT NULL
ORDER BY period_date DESC NULLS LAST)
WHERE rownum <= 5;
So, it will return what rows it has, up to 5, and NULL for the other rows which it does not have, up to 5.......
Thanks in advance
Upvotes: 0
Views: 58
Reputation: 783
Try using a Common Table Expression/Subquery Factoring to generate rows for each year value. Use a RIGHT JOIN
to generate NULL
s for any missing rows.
Normally I would use a LEFT JOIN
. But in this case I think it reads better this way.
Use NVL
to substitute the year
for NULL
period_date
values.
with years as
(
select to_char(sysdate, 'YYYY') as year from dual
UNION ALL
select to_char(add_months(sysdate,-12), 'YYYY') as year from dual
UNION ALL
select to_char(add_months(sysdate,-24), 'YYYY') as year from dual
UNION ALL
select to_char(add_months(sysdate,-36), 'YYYY') as year from dual
UNION ALL
select to_char(add_months(sysdate,-48), 'YYYY') as year from dual
)
SELECT
NVL(TO_CHAR(LAST_DAY(pd.period_date),'YYYYMM'),y.year) as period_date,
pd.actual_eps
FROM period_data pd
RIGHT JOIN years y ON y.year = to_char(pd.period_date,'YYYY')
AND pd.ticker = 'ADRO'
AND pd.period_type = 'A'
AND pd.actual_eps IS NOT NULL
WHERE rownum <= 5
ORDER BY period_date desc, actual_eps nulls last;
Output:
| PERIOD_DATE | ACTUAL_EPS |
|-------------|------------|
| 201902 | foo |
| 201802 | foo |
| 201702 | foo |
| 2016 | (null) |
| 2015 | (null) |
Upvotes: 1