I need to use a where clause within an over clause. How?
SELECT SUM(amount) OVER(WHERE dateval > 12)
Or something like that.
--EDIT--
More details
My table is formatted with a year, month, and amount column.
I want to select all the year, month, and amount rows AND create a fourth 'virtual column' that has the sum of the past 12 months of amount column.
For example:
YEAR | MONTH | AMOUNT
2001 | 03 | 10
2001 | 05 | 25
2001 | 07 | 10
Should create:
YEAR | MONTH | AMOUNT | ROLLING 12 MONTHS
2001 | 03 | 10 | 10
2001 | 05 | 25 | 35
2001 | 07 | 10 | 45
Given a query against your three-column resultset, does the below work for you?
SELECT
SUM(amount) OVER(ORDER BY YEAR ASC, MONTH ASC
ROWS BETWEEN 11 PRECEDING AND CURRENT ROW)
...
select a,(select sum(a) from foo fa where fa.a > fb.a) from foo fb;
Doesn't use over, is pretty inefficient since it is running new sub-query for each query, but it works.
Related
create table your_table(type text,compdate date,amount numeric);
insert into your_table values
('A','2022-01-01',50),
('A','2022-02-01',76),
('A','2022-03-01',300),
('A','2022-04-01',234),
('A','2022-05-01',14),
('A','2022-06-01',9),
('B','2022-01-01',201),
('B','2022-02-01',33),
('B','2022-03-01',90),
('B','2022-04-01',41),
('B','2022-05-01',11),
('B','2022-06-01',5),
('C','2022-01-01',573),
('C','2022-02-01',77),
('C','2022-03-01',109),
('C','2022-04-01',137),
('C','2022-05-01',405),
('C','2022-06-01',621);
I am trying to calculate to show the percentage change in $ from 6 months prior to today's date for each type. In example:
Type A decreased -82% over six months.
Type B decreased -97.5%
Type C increased +8.4%.
How do I write this in postgresql mixed in with other statements?
It looks like comparing against 5, not 6 months prior, and 2022-06-01 isn't today's date.
Join the table with itself based on the matching type and desired time difference. Demo
select
b.type,
b.compdate,
a.compdate "6 months earlier",
b.amount "amount 6 months back",
round(-(100-b.amount/a.amount*100),2) "change"
from your_table a
inner join your_table b
on a.type=b.type
and a.compdate = b.compdate - '5 months'::interval;
-- type | compdate | 6 months earlier | amount 6 months back | change
--------+------------+------------------+----------------------+--------
-- A | 2022-06-01 | 2022-01-01 | 9 | -82.00
-- B | 2022-06-01 | 2022-01-01 | 5 | -97.51
-- C | 2022-06-01 | 2022-01-01 | 621 | 8.38
I have this table inside my postgresql database,
item_code | date | price
==============================
aaaaaa.1 |2019/12/08 | 3.04
bbbbbb.b |2019/12/08 | 19.48
261893.c |2019/12/08 | 7.15
aaaaaa.1 |2019/12/17 | 4.15
bbbbbb.2 |2019/12/17 | 20
xxxxxx.5 |2019/03/12 | 3
xxxxxx.5 |2019/03/18 | 4.5
how can i calculate the average per item, per month over the year. so i get the result something like:
item_code | month | price
==============================
aaaaaa.1 | 2019/12 | 3.59
bbbbbb.2 | 2019/12 | 19.74
261893.c | 2019/12 | 7.15
xxxxxx.5 | 2019/03 | 3.75
I have tried to look and apply many alternatives but i am still not get the point, would really appreciate your help because i am new to postgresql.
I don't see how the question relates to a moving average. It seems you just want group by:
select item_code, date_trunc('month', date) as date_month, avg(price) as price
from mytable
group by item_code, date_month
This gives date_month as a date, truncated to the first day of the month - which I find more useful that the format you suggested. But it you do want that:
to_char(date, 'YYYY/MM') as date_month
I have specific task, and don't know how to realize it. I hope someone can help me =)
I have stock_move table:
product_id |location_id |location_dest_id |product_qty |date_expected |
-----------|------------|-----------------|------------|--------------------|
327 |80 |84 |10 |2014-05-28 00:00:00 |
327 |80 |84 |10 |2014-05-23 00:00:00 |
327 |80 |84 |10 |2014-02-26 00:00:00 |
327 |80 |85 |10 |2014-02-21 00:00:00 |
327 |80 |84 |10 |2014-02-12 00:00:00 |
327 |84 |85 |20 |2014-02-06 00:00:00 |
322 |84 |80 |120 |2015-12-16 00:00:00 |
322 |80 |84 |30 |2015-12-10 00:00:00 |
322 |80 |84 |30 |2015-12-04 00:00:00 |
322 |80 |84 |15 |2015-11-26 00:00:00 |
i.e. it's table of product moves from one warehouse to second.
I can calculate stock at custom date if I use something like this:
select
coalesce(si.product_id, so.product_id) as "Product",
(coalesce(si.stock, 0) - coalesce(so.stock, 0)) as "Stock"
from
(
select
product_id
,sum(product_qty * price_unit) as stock
from stock_move
where
location_dest_id = 80
and date_expected < now()
group by product_id
) as si
full outer join (
select
product_id
,sum(product_qty * price_unit) as stock
from stock_move
where
location_id = 80
and date_expected < now()
group by product_id
) as so
on si.product_id = so.product_id
Result I have current stock:
Product |Stock |
--------|------|
325 |1058 |
313 |34862 |
304 |2364 |
BUT what to do if I need stock per month?
something like this?
Month |Total Stock |
--------|------------|
Jan |130238 |
Feb |348262 |
Mar |2323364 |
How can I sum product qty from start period to end of each month?
I have just one idea - it's use 24 sub queries for get stock per each month (ex. below)
Jan |Feb | Mar |
----|----|-----|
123 |234 |345 |
End after this rotate rows and columns?
I think this's stupid, but I don't know another way... Help me pls =)
Something like this could give you monthly "ending" inventory snapshots. The trick is your data may omit certain months for certain parts, but that part will still have a balance (ie 50 received in January, nothing happened in February, but you still want to show February with a running total of 50).
One way to handle this is to come up with all possible combinations part/dates. I assumed 1/1/14 + 24 months in this example, but that's easily changed in the all_months subquery. For example, you may only want to start with the minimum date from the stock_move table.
with all_months as (
select '2014-01-01'::date + interval '1 month' * generate_series(0, 23) as month_begin
),
stock_calc as (
select
product_id, date_expected,
date_trunc ('month', date_expected)::date as month_expected,
case
when location_id = 80 then -product_qty * price_unit
when location_dest_id = 80 then product_qty * price_unit
else 0
end as qty
from stock_move
union all
select distinct
s.product_id, m.month_begin::date, m.month_begin::date, 0
from
stock_move s
cross join all_months m
),
running_totals as (
select
product_id, date_expected, month_expected,
sum (qty) over (partition by product_id order by date_expected) as end_qty,
row_number() over (partition by product_id, month_expected
order by date_expected desc) as rn
from stock_calc
)
select
product_id, month_expected, end_qty
from running_totals
where
rn = 1
Given this scenario , I have the following employment records
id | user_id | Month | Active
1 | 1 | June 2014 | true
2 | 1 | September 2014 | false
3 | 2 | June 2014 | true
How can i make a query to return the total active users for each month, the return data should be:
active_count | month
2 | June 2014
2 | July 2014
2 | August 2014
1 | September 2014
Any help is highly appreciated
You are looking for a conditional aggregate:
SELECT count(case when active then 1 end) as active_count,
month
FROM employment
GROUP BY month;
With Postgres 9.4 this can be written a bit more concise using the filter() operator:
SELECT count(*) filter (where active) as active_count,
month
FROM employment
GROUP BY month;
It is sql query try to this
SELECT
count(id) active_count,
month
FROM
employment
GROUP BY
month;
Is there a function that calculates the total count of the complete month like below? I am not sure if postgres. I am looking for the grand total value.
2012-08=# select date_trunc('day', time), count(distinct column) from table_name group by 1 order by 1;
date_trunc | count
---------------------+-------
2012-08-01 00:00:00 | 22
2012-08-02 00:00:00 | 34
2012-08-03 00:00:00 | 25
2012-08-04 00:00:00 | 30
2012-08-05 00:00:00 | 27
2012-08-06 00:00:00 | 31
2012-08-07 00:00:00 | 23
2012-08-08 00:00:00 | 28
2012-08-09 00:00:00 | 28
2012-08-10 00:00:00 | 28
2012-08-11 00:00:00 | 24
2012-08-12 00:00:00 | 36
2012-08-13 00:00:00 | 28
2012-08-14 00:00:00 | 23
2012-08-15 00:00:00 | 23
2012-08-16 00:00:00 | 30
2012-08-17 00:00:00 | 20
2012-08-18 00:00:00 | 30
2012-08-19 00:00:00 | 20
2012-08-20 00:00:00 | 24
2012-08-21 00:00:00 | 20
2012-08-22 00:00:00 | 17
2012-08-23 00:00:00 | 23
2012-08-24 00:00:00 | 25
2012-08-25 00:00:00 | 35
2012-08-26 00:00:00 | 18
2012-08-27 00:00:00 | 16
2012-08-28 00:00:00 | 11
2012-08-29 00:00:00 | 22
2012-08-30 00:00:00 | 26
2012-08-31 00:00:00 | 17
(31 rows)
--------------------------------
Total | 12345
As best I can guess from your question and comments you want sub-totals of the distinct counts by month. You can't do this with group by date_trunc('month',time) because that'll do a count(distinct column) that's distinct across all days.
For this you need a subquery or CTE:
WITH day_counts(day,day_col_count) AS (
select date_trunc('day', time), count(distinct column)
from table_name group by 1
)
SELECT 'Day', day, day_col_count
FROM day_counts
UNION ALL
SELECT 'Month', date_trunc('month', day), sum(day_col_count)
FROM day_counts
GROUP BY 2
ORDER BY 2;
My earlier guess before comments was: Group by month?
select date_trunc('month', time), count(distinct column)
from table_name
group by date_trunc('month', time)
order by time
Or are you trying to include running totals or subtotal lines? For running totals you need to use sum as a window function. Subtotals are just a pain, as SQL doesn't really lend its self to them; you need to UNION two queries then wrap them in an outer ORDER BY.
select
date_trunc('day', time)::text as "date",
count(distinct column) as count
from table_name
group by 1
union
select
'Total',
count(distinct column)
from table_name
group by 1, date_trunc('month', time)
order by "date" = 'Total', 1