PIVOT by timestamp - sql

Question

I need to pivot the time (hour) for the results of this query, so there could be up to 24 hours running across the top as you can replicate in an Excel pivot table. Is there an easy and efficient way to replicate this in the query?
Query:
SELECT
trunc(complete_dstamp) "Date",
to_char(complete_dstamp, 'HH24') "Hour",
user_id "User",
sum(update_qty) "Qty"
FROM
inventory_transaction
WHERE
to_loc_id = 'CONTAINER'
and trunc(complete_dstamp) > (
trunc(current_timestamp)-1
)
GROUP BY
trunc(complete_dstamp),
to_char(complete_dstamp, 'HH24'),
user_id
ORDER BY
1,
2
Desired output:
Current output:

Answer 1

Use conditional aggregation:
SELECT TO_CHAR(complete_dstamp, 'YYYY-MM-DD') AS "Date",
user_id AS "User",
SUM(CASE TO_CHAR(complete_dstamp, 'HH24') WHEN '00' THEN update_qty END) AS hour_0,
SUM(CASE TO_CHAR(complete_dstamp, 'HH24') WHEN '01' THEN update_qty END) AS hour_1,
SUM(CASE TO_CHAR(complete_dstamp, 'HH24') WHEN '02' THEN update_qty END) AS hour_2,
SUM(CASE TO_CHAR(complete_dstamp, 'HH24') WHEN '03' THEN update_qty END) AS hour_3,
SUM(CASE TO_CHAR(complete_dstamp, 'HH24') WHEN '04' THEN update_qty END) AS hour_4,
-- ...
SUM(CASE TO_CHAR(complete_dstamp, 'HH24') WHEN '23' THEN update_qty END) AS hour_23,
sum(update_qty) AS grand_total
FROM inventory_transaction
WHERE to_loc_id = 'CONTAINER'
AND complete_dstamp >= trunc(current_timestamp)
GROUP BY
TO_CHAR(complete_dstamp, 'YYYY-MM-DD'),
user_id
ORDER BY
"Date",
"User"
Or a PIVOT:
SELECT "Date",
user_id AS "User",
hour_0,
hour_1,
hour_2,
hour_3,
hour_4,
-- ...
hour_23,
COALESCE(hour_0, 0)
+ COALESCE(hour_1, 0)
+ COALESCE(hour_2, 0)
+ COALESCE(hour_3, 0)
+ COALESCE(hour_4, 0)
-- ...
+ COALESCE(hour_23, 0) AS grand_total
FROM (
SELECT TO_CHAR(complete_dstamp, 'YYYY-MM-DD') AS "Date",
TO_CHAR(complete_dstamp, 'HH24') AS hour,
user_id,
update_qty
FROM inventory_transaction
WHERE to_loc_id = 'CONTAINER'
AND complete_dstamp >= trunc(current_timestamp)
)
PIVOT (
SUM(update_qty) FOR hour IN (
'00' AS hour_0,
'01' AS hour_1,
'02' AS hour_2,
'03' AS hour_3,
'04' AS hour_4,
-- ...
'23' AS hour_23
)
)
db<>fiddle here