I need to get result from Existing Table and my current result is like
Code Name Branch Dr Cr Net
1001 Closing Stock UP 150000 195000 -45000
1001 Closing Stock DL 159.74 0 159.74
1001 Closing Stock CH 0 24.37 -24.37
1002 IGST Payable UP 0 135.37 -135.37
1002 IGST Payable DL 0 200 -200
1002 IGST Payable CH 200 0 200
1003 Sundry Debtors UP 15767000 0 15767000
1003 Sundry Debtors DL 0 181716 -181716
Note :
If Branches Increase then Branch wise (Debit,Credit,Net) Fields should be Create automatically.
And Result should be like this
Code Name UPDr UPCr UPNet CHDr CHCr CHNet DLDr DLCr DLNet
1001 Stk 1500 1950 -450 0 24.37 -24.37 159 0 159.74
1002 IGST 0 135.37 -135 200 0 200 0 181 -181
1003 Sund 157 0 157 159 0 159 200 0 200
Please help for the same.
Related
I have 2 tables:
Table 1 "contract" is as the following (sample only):
contract_id
contract_status
111
Active
122
Active
133
Finished
144
Active
155
Finished
contract_status values are: Active and Finished
Table 2 "ledger" is as the following (sample only):
ledger_id
contract_id
ledger_status
amount
1gh
111
WAIVED
450
2uk
111
PAID
0
2jz
111
WAIVED
200
4bc
122
PAID
0
5jw
122
UNPAID
150
3xs
133
PAID
0
9kd
133
WAIVED
250
7bf
144
WAIVED
100
8aq
155
UNPAID
700
One contract can have a lot of ledger_id
How can I calculate the total amount that has been waived for each contract status (active and finished)?
Expected output
contract_status
total_amount_waived
Active
750
Finished
250
We can try a join aggregation approach:
SELECT c.contract_status, COALESCE(SUM(l.amount), 0) AS total_amount_waived
FROM contract c
LEFT JOIN ledger l
ON l.contract_id = c.contract_id AND
l.ledger_status = 'WAIVED'
GROUP BY c.contract_status;
I have a requirement where I need to link all related CUSTOMER ID and assign a Unified Cust ID to all the related Cust_id.
Ex: for below data,
INPUT DATA
PK_ID CUST_ID_1 CUST_ID_2 CUST_ID_3
1 123 456 567
2 898 567 780
3 999 780 111
4 111 222 333
Based on CUST_ID_1/CUST_ID_2/CUST_ID_3 need to link all the and assign a Unified ID to all the rows.
OUTPUT DATA
Unified ID CUST_ID_1 CUST_ID_2 CUST_ID_3
1000 123 456 567
1000 898 567 780
1000 999 780 111
1000 111 222 333
Trying to perform Self Join but it cannot be definite. Is there a function or ANSI SQL feature which can help in this?
What i have tried,
CREATE TEMP TBL_TEMP AS(
SELECT A.PK_ID
FROM TBL A
LEFT JOIN TBL B
ON A.CUST_ID_1=B.CUST_ID_1
AND A.PK_ID<>B.PK_ID)
UPDATE TBL
FROM TBL_TEMP
SET UNIFIED_ID=SEQ_UNIF_ID.nextval
WHERE TBL.PK_ID=TBL_TEMP.PK_ID
This update i have to write for each column and multiple times.
If you are ok with gap in sequences then following is what I can come up with as of now.
update cust_temp a
set unified_id = t.unified_id
from
(
select
case
when (select count(*) from cust_temp b
where arrays_overlap(array_construct(a.cust_id_1,a.cust_id_2,a.cust_id_3),
array_construct(b.cust_id_1,b.cust_id_2,b.cust_id_3)))>1 -- match across data-set
then 1000 -- same value for common rows
else
ts.nextval --- using sequence for non-common rows
end unified_id,
a.cust_id_1,a.cust_id_2,a.cust_id_3
from cust_temp a, table(getnextval(SEQ_UNIF_ID)) ts) t
where t.cust_id_1 = a.cust_id_1
and t.cust_id_2 = a.cust_id_2
and t.cust_id_3 = a.cust_id_3;
Updated data-set
select * from cust_temp;
UNIFIED_ID
CUST_ID_1
CUST_ID_2
CUST_ID_3
1000
123
456
567
1000
898
567
780
1000
111
222
333
20000
100
200
300
1000
999
780
111
1000
234
123
901
23000
260
360
460
24000
160
560
760
Original data set -
select * from cust_temp;
UNIFIED_ID
CUST_ID_1
CUST_ID_2
CUST_ID_3
NULL
123
456
567
NULL
898
567
780
NULL
111
222
333
NULL
100
200
300
NULL
999
780
111
NULL
234
123
901
NULL
260
360
460
NULL
160
560
760
Arrays_overlap logic is thanks to #Simeon.
Following procedure can be used -
EXECUTE IMMEDIATE $$
DECLARE
duplicate number;
x number;
BEGIN
duplicate := (select count(cnt) from (select a.unified_id,count(*) cnt from cust_temp a,
cust_temp b
where
arrays_overlap(array_construct(a.cust_id_1,a.cust_id_2,a.cust_id_3),
array_construct(b.cust_id_1,b.cust_id_2,b.cust_id_3))
AND a.cust_id_1 != b.cust_id_1
AND a.cust_id_2 != b.cust_id_2
AND a.cust_id_3 != b.cust_id_3
group by a.unified_id) where cnt>1
);
for x in 1 to duplicate do
update cust_temp a
set a.unified_id = (select min(b.unified_id) uid from cust_temp b
where arrays_overlap(array_construct(a.cust_id_1,a.cust_id_2,a.cust_id_3),
array_construct(b.cust_id_1,b.cust_id_2,b.cust_id_3)));
end for;
END;
$$
;
Which will produce following output dataset -
UNIFIED_ID
CUST_ID_1
CUST_ID_2
CUST_ID_3
1000
100
200
300
2000
123
456
567
2000
898
567
780
2000
111
222
333
2000
999
780
111
2000
234
123
901
7000
260
360
460
8000
160
560
760
8000
186
160
766
For an input data-set as -
UNIFIED_ID
CUST_ID_1
CUST_ID_2
CUST_ID_3
1000
100
200
300
2000
123
456
567
3000
898
567
780
4000
111
222
333
5000
999
780
111
6000
234
123
901
7000
260
360
460
8000
160
560
760
9000
186
160
766
I've got a query with three Cross-Applies that gather data from three different tables. The first Cr-Ap assists the 2nd and 3rd Cr-Ap's. It finds the most recent ID of a certain refill for a 'cartridge', the higher the ID the more recent the refill.
The second and third Cr-Ap's gather the SUMS of items that have been refilled and items that have been dispensed under the most recent Refill.
If I run the query for Cr-Ap 2 or 3 separately the output would look something like:
ID Amount
1 100
2 1000
3 100
4 0
5 0
etc
Amount would be either the amount of dispensed or refilled items.
Only I don't want to run these queries separately, I want them next to each other.
So what I want is a table that looks like this:
ID Refill Dispense
1 100 1
2 1000 5
3 100 7
4 0 99
5 0 3
etc
My gut tells me to do
INNER JOIN crossaply2 ON crossapply3.ID = crossapply2.ID
But this doesn't work. I'm still new to SQL so I don't exactly know what I can and can't join, what I do know is that you can use crossapply as a join (sorta?). I think that might be what I need to do here, I just don't know how.
But that's not it, there's another complication, there are certain refills where nothing gets dispensed. In these scenarios the crossapply I wrote for dispenses won't return anything for that refillID. With nothing I don't mean NULL, I mean it just skips the refillID. But I'd like to see a 0 in those cases. Because it just skips over those ID's I can't get COALESCE or ISNULL to work, this might also complicate the joining of these two applies. Because an INNER JOIN would skip any line where there is no Dispensed amount, even though there is a Refilled amount Id like to see.
Here is my code:
-- Dispensed SUM and Refilled SUM combined
SELECT [CartridgeRefill].[FK_CartridgeRegistration_Id]
,Refills.Refilled
,Dispenses.Dispensed
FROM [CartridgeRefill]
CROSS APPLY(
SELECT MAX([CartridgeRefill].[Id]) AS RecentRefillID
FROM [CartridgeRefill]
GROUP BY [CartridgeRefill].[FK_CartridgeRegistration_Id]
) AS RecentRefill
CROSS APPLY(
SELECT [CartridgeRefill].[FK_CartridgeRegistration_Id] AS RefilledID
,SUM([CartridgeRefillMedication].[Amount]) AS Refilled
FROM [CartridgeRefillMedication]
INNER JOIN [CartridgeRefill] ON [CartridgeRefillMedication].[FK_CartridgeRefill_Id] = [CartridgeRefill].[Id]
WHERE [CartridgeRefillMedication].[FK_CartridgeRefill_Id] = RecentRefill.RecentRefillID
GROUP BY [CartridgeRefill].[FK_CartridgeRegistration_Id]
) AS Refills
CROSS APPLY(
SELECT [CartridgeRefill].[FK_CartridgeRegistration_Id] AS DispensedID
,SUM([CartridgeDispenseAttempt].[Amount]) AS Dispensed
FROM [CartridgeDispenseAttempt]
INNER JOIN [CartridgeRefill] ON [CartridgeDispenseAttempt].[FK_CartridgeRefill_Id] = [CartridgeRefill].[Id]
WHERE [CartridgeDispenseAttempt].[FK_CartridgeRefill_Id] = RecentRefill.RecentRefillID
GROUP BY [CartridgeRefill].[FK_CartridgeRegistration_Id]
) AS Dispenses
GO
The output of this code is as follows:
1 300 1
1 300 1
1 200 194
1 200 194
1 200 8
1 200 8
1 0 39
1 0 39
1 100 14
1 100 14
1 200 1
1 200 1
1 0 28
1 0 28
1 1000 102
1 1000 102
1 1000 557
1 1000 557
1 2000 92
1 2000 92
1 100 75
1 100 75
1 100 100
1 100 100
1 100 51
1 100 51
1 600 28
1 600 28
1 200 47
1 200 47
1 200 152
1 200 152
1 234 26
1 234 26
1 0 227
1 0 227
1 10 6
1 10 6
1 300 86
1 300 86
1 0 194
1 0 194
1 500 18
1 500 18
1 1000 51
1 1000 51
1 1000 56
1 1000 56
1 500 48
1 500 48
1 0 10
1 0 10
1 1500 111
1 1500 111
1 56 79
1 56 79
1 100 6
1 100 6
1 44 134
1 44 134
1 1000 488
1 1000 488
1 100 32
1 100 32
1 100 178
1 100 178
1 500 672
1 500 672
1 200 26
1 200 26
1 500 373
1 500 373
1 100 10
1 100 10
1 900 28
1 900 28
2 900 28
2 900 28
2 900 28
etc
It is total nonsense that I can't do much with, it goes on for about 20k lines and goes through all the ID's, eventually.
Any help is more than appreciated :)
Looks like overcomplicated a bit.
Try
WITH cr AS (
SELECT [FK_CartridgeRegistration_Id]
,MAX([CartridgeRefill].[Id]) RecentRefillID
FROM [CartridgeRefill]
GROUP BY [FK_CartridgeRegistration_Id]
)
SELECT cr.[FK_CartridgeRegistration_Id], Refills.Refilled, Dispenses.Dispensed
FROM cr
CROSS APPLY(
SELECT SUM(crm.[Amount]) AS Refilled
FROM [CartridgeRefillMedication] crm
WHERE crm.[FK_CartridgeRefill_Id] = cr.RecentRefillID
) AS Refills
CROSS APPLY(
SELECT SUM(cda.[Amount]) AS Dispensed
FROM [CartridgeDispenseAttempt] cda
WHERE cda.[FK_CartridgeRefill_Id] = cr.RecentRefillID
) AS Dispenses;
using pandas for the first time and I've provided a smaller version of a data frame i've created like the following:
Date project1 project2 project3
0 12/10/2017 100 200 300
1 12/11/2017 0 100 100
2 12/12/2017 0 0 100
I need to do 2 cumsum for each seperate project where it stops at the zero. And another across all of the projects row wise. I keep struggling with either the date or just counting the zeros. Any advice would be appreciates.
So the output would like:
Date project1 project2 project3
0 12/10/2017 100 200 300
1 12/11/2017 0 300 400
2 12/12/2017 0 0 500
and
Date project1 project2 project3 project_sum
0 12/10/2017 100 200 300 600
1 12/11/2017 0 300 400 700
2 12/12/2017 0 0 500 500
For you 1st question , using cumsum and cumprod
df[['project1','project2','project3']].cumsum().mask(df[['project1','project2','project3']].cumprod().eq(0),0)
Out[86]:
project1 project2 project3
0 100 200 300
1 0 300 400
2 0 0 500
And then assign it back using sum(axis=1)
df[['project1','project2','project3']]=df[['project1','project2','project3']].cumsum().mask(df[['project1','project2','project3']].cumprod().eq(0),0)
df['projectSum']=df[['project1','project2','project3']].sum(1)
df
Out[89]:
Date project1 project2 project3 projectSum
0 12/10/2017 100 200 300 600
1 12/11/2017 0 300 400 700
2 12/12/2017 0 0 500 500
Say I have the following data in my table;
tran_date withdraw deposit
25/11/2010 0 500
2/12/2010 100 0
15/12/2010 0 300
18/12/2010 0 200
25/12/2010 200 0
Suppose I want to get the following for date range between 1/12/2010 and 31/12/2010.
tran_date withdraw deposit balance days_since_last_tran
1/12/2010 0 0 500 0
2/12/2010 100 0 400 1
15/12/2010 0 300 700 13
18/12/2010 0 200 900 3
25/12/2010 200 0 700 7
31/12/2010 0 0 700 6
Is this doable in PostgreSQL 8.4?
Use:
SELECT t.tran_date,
t.withdraw,
t.deposit,
(SELECT SUM(y.deposit) - SUM(y.withdrawl)
FROM YOUR_TABLE y
WHERE y.tran_date <= t.tran_date) AS balance,
t.tran_date - COALESCE(LAG(t.tran_date) OVER(ORDER BY t.tran_date),
t.tran_date) AS days_since_last
FROM YOUR_TABLE t
8.4+ is nice, providing access to analytic/windowing functions like LAG.