I am trying to create a stock holding based on the below data.
Input and Desired Output
I have tried using creating a transactions column (Starting + UK Open POs - UK Sales).
Then used the below SQL code to create a stock holding.
Sum OVER ( TRANSACTIONS)
[ <PARTITION BY No_ ]
[ <ORDER BY Date ]
But the problem is I don't want the stock holding to go into a negative. I want it to show 0, so when 960 units come in on 14/04/19 the stock holding is 921 units (960-39) instead of 116 units.
The column highlighted in yellow is my desired output. I need this over 5k SKUs (column no_)
Any help would be very appreciated.
No_ Date UK-Open PO UK-Sales Starting Stock Trans. Cumul Stock Stock Level
111111 22/03/2019 47 100 53 53 53
111111 24/03/2019 330 -330 -277 0
111111 31/03/2019 443 -443 -720 0
111111 07/04/2019 85 -85 -805 0
111111 14/04/2019 960 39 921 116 921
111111 21/04/2019 960 112 848 964 1769
111111 28/04/2019 100 -100 864 1669
111111 05/05/2019 504 -504 360 1165
111111 12/05/2019 606 -606 -246 559
111111 19/05/2019 118 -118 -364 441
111111 26/05/2019 400 -400 -764 41
111111 02/06/2019 674 -674 -1438 0
111111 09/06/2019 338 -338 -1776 0
111111 16/06/2019 206 -206 -1982 0
111111 23/06/2019 115 -115 -2097 0
111111 30/06/2019 500 66 434 -1663 434
111111 07/07/2019 33 -33 -1696 401
Suppressing the negative numbers as you are doing requires remembering what has happened on all previous rows. Alas, this can't be done using window function.
The alternative is a recursive CTE:
with t as (
select no_, date, starting_stock, trans,
row_number() over (partition by no_ order by date) as seqnum
from <table>
),
cte as (
select no_, date, trans, seqnum,
starting_stock as stock_level
from t
where seqnum = 1
union all
select t.no_, t.date, t.trans, t.seqnum,
(case when cte.starting_stock + t.trans < 0 then 0
else cte.starting_stock + t.trans
end) as stock_level
from cte join
t
on t.seqnum = cte.seqnum + 1 and
t.no_ = cte.no_
)
select *
from cte
option (maxrecursion 0);
You only need the option if the number of rows exceeds 100 from the recursion.
Related
I have a query:
SELECT
date,
COUNT(o.row_number)FILTER (WHERE o.row_number > 1 AND date_ddr IS NOT NULL AND telephone_number <> 'Anonymous' ) repeat_calls_24h
(
SELECT
telephone_number,
date_ddr,
ROW_NUMBER() OVER(PARTITION BY ddr.telephone_number ORDER BY ddr.date) row_number,
FROM
table_a
)o
GROUP BY 1
Generating the following table:
date
Repeat calls_24h
17/09/2022
182
18/09/2022
381
19/09/2022
81
20/09/2022
24
21/09/2022
91
22/09/2022
110
23/09/2022
231
What can I add to my query to provide a sum of the previous three days as below?:
date
Repeat calls_24h
Repeat Calls 3d
17/09/2022
182
18/09/2022
381
19/09/2022
81
644
20/09/2022
24
486
21/09/2022
91
196
22/09/2022
110
225
23/09/2022
231
432
Thanks
We can do it using lag.
select "date"
,"Repeat calls_24h"
,"Repeat calls_24h" + lag("Repeat calls_24h") over(order by "date") + lag("Repeat calls_24h", 2) over(order by "date") as "Repeat Calls 3d"
from t
date
Repeat calls_24h
Repeat Calls 3d
2022-09-17
182
null
2022-09-18
381
null
2022-09-19
81
644
2022-09-20
24
486
2022-09-21
91
196
2022-09-22
110
225
2022-09-23
231
432
Fiddle
I'm trying to select some values based on some proprietary data, and I just changed the variables to reference house prices.
I am trying to get the total offers for houses where they were sold at the bid or at the ask price, with offers under 15 and offers * sale price less than 5,000,000.
I then want to get the total number of offers for each neighborhood on each day, but instead I'm getting the total offers across each neighborhood (n1 + n2 + n3 + n4 + n5) across all dates and the total offers in the dataset across all dates.
My current query is this:
SELECT DISTINCT(neighborhood),
DATE(date_of_sale),
(SELECT SUM(offers)
FROM `big_query.a_table_name.houseprices`
WHERE ((offers * accepted_sale_price < 5000000)
AND (offers < 15)
AND (house_bid = sale_price OR
house_ask = sale_price))) as bid_ask_off,
(SELECT SUM(offers)
FROM `big_query.a_table_name.houseprices`) as
total_offers,
FROM `big_query.a_table_name.houseprices`
GROUP BY neighborhood, DATE(date_of_sale) LIMIT 100
Which I am expecting a result like, with date being repeated throughout as d1, d2, d3, etc.:
but am instead receiving
I'm aware that there are some inherent problems with what I'm trying to select / group, but I'm not sure what to google or what tutorials to look at in order to perform this operation.
It's querying quite a bit of data, and I want to keep costs down, as I've already racked up a smallish bill on queries.
Any help or advice would be greatly appreciated, and I hope I've provided enough information.
Here is a sample dataframe.
neighborhood date_of_sale offers accepted_sale_price house_bid house_ask
bronx 4/1/2022 3 323 320 323
manhattan 4/1/2022 4 244 230 244
manhattan 4/1/2022 8 856 856 900
queens 4/1/2022 15 110 110 135
brooklyn 4/2/2022 12 115 100 115
manhattan 4/2/2022 9 255 255 275
bronx 4/2/2022 6 330 300 330
queens 4/2/2022 10 405 395 405
brooklyn 4/2/2022 4 254 254 265
staten_island 4/3/2022 2 442 430 442
staten_island 4/3/2022 13 195 195 225
bronx 4/3/2022 4 650 650 690
manhattan 4/3/2022 2 286 266 286
manhattan 4/3/2022 6 356 356 400
staten_island 4/4/2022 4 361 361 401
staten_island 4/4/2022 5 348 348 399
bronx 4/4/2022 8 397 340 397
manhattan 4/4/2022 9 333 333 394
manhattan 4/4/2022 11 392 325 392
I think that this is what you need.
As we group by neighbourhood we do not need DISTINCT.
We take sum(offers) for total_offers directly from the table and bids from a sub-query which we join to so that it is grouped by neighbourhood.
SELECT
h.neighborhood,
DATE(h.date_of_sale) AS date_,
s.bids AS bid_ask_off,
SUM(h.offers) AS total_offers,
FROM
`big_query.a_table_name.houseprices` h
LEFT JOIN
(SELECT
neighborhood,
SUM(offers) AS bids
FROM
`big_query.a_table_name.houseprices`
WHERE offers * accepted_sale_price < 5000000
AND offers < 15
AND (house_bid = sale_price OR
house_ask = sale_price)
GROUP BY neighborhood) s
ON h.neighborhood = s.neighborhood
GROUP BY
h.neighborhood,
DATE(date_of_sale),
s.bids
LIMIT 100;
Or the following which modifies more the initial query but may be more like what you need.
SELECT
h.neighborhood,
DATE(h.date_of_sale) AS date_,
s.bids AS bid_ask_off,
SUM(h.offers) AS total_offers,
FROM
`big_query.a_table_name.houseprices` h
LEFT JOIN
(SELECT
date_of_sale dos,
neighborhood,
SUM(offers) AS bids
FROM
`big_query.a_table_name.houseprices`
WHERE offers * accepted_sale_price < 5000000
AND offers < 15
AND (house_bid = sale_price OR
house_ask = sale_price)
GROUP BY
neighborhood,
date_of_sale) s
ON h.neighborhood = s.neighborhood
AND h.date_of_sale = s.dos
GROUP BY
h.neighborhood,
DATE(date_of_sale),
s.bids
LIMIT 100;
I have the following table :
cohort
month cohort
orders
cumulated orders
2021-01
0
126
126
2021-01
1
5
131
2021-01
2
4
135
2021-02
0
131
131
2021-02
1
9
140
2021-02
2
8
148
And now I want to have the following table where I divide each repeat orders by the number of orders of month 0 :
cohort
month cohort
orders
cumulated orders
cumulated in %
2021-01
0
126
126
100%
2021-01
1
5
131
104%
2021-01
2
4
135
107%
2021-02
0
131
131
100%
2021-02
1
9
140
107%
2021-02
2
8
148
114%
My only hint is to create a CASE statement, but I don't want each month to update the query by adding the line
WHEN cohort="2021-08" THEN cumulated orders / 143
where 143 is the number of orders of cohort 2021-08 at month cohort =0
Has someone got an idea how to get this table ?
A case expression isn't needed. You can use first_value():
select t.*,
( cumulated_order /
first_value(orders) over (partition by cohort order by month_cohort)
) as ratio
from t;
If you really wanted a case, you could use:
select t.*,
( cumulated_order /
max(case when month_cohort = 0 then orders end) over (partition by cohort)
) as ratio
from t;
Consider below
select *,
round(100 * cumulated_orders /
sum(if(month_cohort = 0, orders, 0)) over(partition by cohort)
) as cumulated_in_percent
from `project.dataset.table`
if applied to sample data in your question - output is
I have a table data like this:
TranNo Acc No Bal Acc Debit Credit
1 511 131 1000
1 521 131 200
1 333 131 80
1 131 511 1000
1 131 521 200
1 131 333 80
Is there a way to select data and order like this? I'm using SQL Server 2012. Thank you!
TranNo Acc No Bal Acc Debit Credit
1 131 511 1000
1 511 131 1000
1 521 131 200
1 131 521 200
1 131 333 80
1 333 131 80
I think so. I think that ordering by the least and greatest of the accounts gets the pairs that you want. Then, additional logic gets the debit before the credit:
select t.*
from table t
order by (case when accno < balacc then accno else balacc end),
(case when accno < balacc then balacc else accno end),
(case when debit > 0 then 1 else 0 end);
I previously found the solution to my problem but unfortunately I lost files on my harddrive and I can't find the statement I managed to produce.
I have 2 tables T2REQ and T2STOCK, both have 2 columns (typeID and quantity) and my problem reside in the fact that I can have multiple occurences of SAME typeID in BOTH tables.
What I'm trying to do is SUM(QUANTITY) grouped by typeID and substract the values of T2STOCK from T2REQ but since I have multiple occurences of same typeID in both tables, the SUM I get is multiplied by the number of occurences of typeID.
Here's a sample of T2REQ (take typeID 11399 for example):
typeID quantity
---------- ----------
34 102900
35 10500
36 3220
37 840
11399 700
563 140
9848 140
11486 28
11688 700
11399 390
4393 130
9840 390
9842 390
11399 390
11483 19.5
11541 780
And this is a sample of T2STOCK table :
typeID quantity
---------- ----------
9842 1921
9848 2400
11399 1700
11475 165
11476 27
11478 28
11481 34
11483 122
11476 2
And this is where I'm at for now, I know that the SUM(t2stock.quantity) is affected (multiplied) because of the JOIN 1 = 1 but whatever I tried, I'm not doing it in the right order:
SELECT
t2req.typeID, sum(t2req.quantity), sum(t2stock.quantity),
sum(t2req.quantity) - sum(t2stock.quantity) as diff
FROM t2req JOIN t2stock ON t2req.typeID = t2stock.typeID
GROUP BY t2req.typeID
ORDER BY diff DESC;
typeID sum(t2req.quantity) sum(t2stock.quantity) diff
---------- ------------------- --------------------- ----------
563 140 30 110
11541 780 780 0
11486 28 40 -12
11483 19.5 122 -102.5
9840 390 1000 -610
40 260 940 -680
9842 390 1921 -1531
9848 140 2400 -2260
11399 1480 5100 -3620
39 650 7650 -7000
37 1230 116336 -115106
36 28570 967098 -938528
35 33770 2477820 -2444050
34 102900 2798355 -2695455
You can see that SUM(t2req) for typeID 11399 is correct : 1480
And you can see that the SUM(t2stock) for typeID 11399 is not correct : 5100 instead of 1700 (which is 5100 divided by 3, the number of occurences in t2req)
What would be the best way to avoid multiplications because of multiple typeIDs (in both tables) with the JOIN for my sum substract ?
Sorry for the wall of text, just trying to explain as best as I can since english is not my mother tongue.
Thanks a lot for your help.
You can aggregate before join:
SELECT
t2req.typeID,
t2req.quantity,
t2stock.quantity,
t2req.quantity - t2stock.quantity as diff
FROM
(SELECT TypeID, SUM(Quantity) Quantity FROM t2req GROUP BY TypeID) t2req JOIN
(SELECT TypeID, SUM(Quantity) Quantity FROM t2stock GROUP BY TypeID) t2stock
ON t2req.typeID = t2stock.typeID
ORDER BY diff DESC;
Fiddle sample: http://sqlfiddle.com/#!7/06711/5
You can't do this in a single aggregation:
SELECT
COALESCE(r.typeID, s.typeID) AS typeID,
COALESCE(r.quantity, 0) AS req_quantity,
COALESCE(s.quantity, 0) AS stock_quantity,
COALESCE(r.quantity, 0) - COALESCE(s.quantity, 0) AS diff
FROM (
SELECT rr.typeID, SUM(rr.quantity) AS quantity
FROM t2req rr
GROUP BY rr.typeID
) r
CROSS JOIN (
SELECT ss.typeID, SUM(ss.quantity) AS quantity
FROM t2stock ss
GROUP BY ss.typeID
) s ON r.typeID = s.typeID
ORDER BY 4 DESC;