I need to create 2 cumulative sums based on the value type, for example:
I have values of incoming stock units from 2 types: A and B. and I also have records of outgoing stock units.
If we have enough stock of type "A" it should taken out of type A, if not- it should be taken out of type B. so basically I need to crate the columns "A stock" and "B stock" below, representing the current balance of each type.
I tried using cumulative sum but I'm having trouble with the condition... is there a way to write this query without using a loop ? ( Vertica DB)
In table below A_stock and B_stock are the final result I need to create
ID Type In OUT A stock B stock Order_id
1 A 100 0 100 0 1
1 B 50 0 100 50 2
1 A 100 0 200 50 3
1 - 0 -200 0 50 4
1 - 0 -10 0 40 5
1 B 50 0 0 90 6
1 A 40 0 40 90 7
1 - 0 -20 20 90 8
2 A 30 0 30 0 1
2 B 20 0 30 20 2
2 A 10 0 40 20 3
2 - 0 -20 20 20 4
You can use window functions - but you need a column that defines the ordering of the rows, I assumed ordering_id:
select t.*,
sum(case when type = 'A' then in + out else 0 end) over(partition by id order by ordering_id) a_stock,
sum(case when type = 'B' then in + out else 0 end) over(partition by id order by ordering_id) b_stock
from mytable t
This assumes that you want the stock on a per-id basis; if that's not the case, just remove the partition clause from the over() clause.
I have a history of customer IDs and purchase IDs where no customer has ever bought the same product. However, for each purchase ID (which is unique), how can I find out the number of times the customer has made a previous purchase
I have tried using groupby() and sort_values()
df = pd.DataFrame({'id_cust': [1,2,1,3,2,4,1],
'id_purchase': ['20A','143C','99B','78R','309D','90J','78J']})
df.sort_values(by='id_cust')
df.groupby('id_cust')['id_purchase'].cumcount()
This is what I expect:
id_cust id_purchase value
1 20A 1
2 143C 1
1 99B 2
3 78R 1
2 3097D 2
4 900J 1
1 78J 3
You can just use the cumcount() on the id_cust column since the id_purchase is unique:
df['value']=df.groupby('id_cust')['id_cust'].cumcount()+1
print(df)
id_cust id_purchase value
0 1 20A 1
1 2 143C 1
2 1 99B 2
3 3 78R 1
4 2 309D 2
5 4 90J 1
6 1 78J 3
I need a dax measure which shows me which customers bought products B and C in last 90 days.
And another one which shows me those whose bought products B and C in last 90 days.
(based in my filter date context)
Below is like it should be:
Can someone help me?
Here is a sample data if needed:
FactSales
KeyDate KeyCustomer KeyProduct Total
1 1 1 12,9
1 2 2 13
1 3 1 156,4
1 4 1 564,8
2 1 1 894,8
2 2 1 56,5
3 1 2 564,85
3 2 3 564,8
4 1 1 1325,6
4 2 1 132,3
Customer
KeyCustomer Name
1 Jean
2 Mari
3 Lisa
4 Julian
5 Jhonny
Calendar
KeyDate Date
1 01/01/2018
2 02/01/2018
3 01/05/2018
4 01/08/2018
Product
KeyProduct Product
1 A
2 B
3 C
Try something along these lines:
IfBought = IF(
COUNTROWS(
FILTER(FactSales,
RELATED('Product'[Product]) IN {"B", "C"} &&
RELATED('Calendar'[Date]) > TODAY() - 90)
) > 0,
1, 0)
Note that May 1st is longer than 90 days ago as of today though, so you won't get the result you asked for unless you change 90 to 114 or greater.
I have a view with Columns:
WeekNo, MerchantId, Transactions
With a Select Query let's say that we have the following results:
TrnWeek AgencyId WeeklyTrn
1 110008 1
2 110008 2
3 110008 2
1 110045 4
3 110065 4
3 110124 1
1 110153 1
1 110155 3
2 110163 1
2 110165 1
making a pivot (stored procedure which creates dynamically the columns) i get the TrnWeek as Columns and as a result the following:
[1] [2] [3]
1 1 1
1 0 0
1 0 0
1 0 0
0 1 1
0 1 0
0 0 1
what I want to get is a "matrix" as follows:
TrnWeek 1 2 3
1 4 1 1
2 0 2 1
3 0 0 1
ih which i calculate how many merchants performed a transaction in the first week (position: 1,1), how many of them performed a transaction in the second one (position: 1,2), how many performed their first transaction in 2nd week (position: 2,2) etc.
I work for a small company and we're trying to get away from Excel workbooks for Inventory control. I thought I had it figured out with help from (Nasser) but its beyond me. This is what I can get into a table, from there I need too get it to look like the table below.
My data
ID|GrpID|InOut| LoadFt | LoadCostft| LoadCost | RunFt | RunCost| AvgRunCostFt
1 1 1 4549.00 0.99 4503.51 4549.00 0 0
2 1 1 1523.22 1.29 1964.9538 6072.22 0 0
3 1 2 -2491.73 0 0 3580.49 0 0
4 1 2 -96.00 0 0 3484.49 0 0
5 1 1 8471.68 1.41 11945.0688 11956.17 0 0
6 1 2 -369.00 0 0 11468.0568 0 0
7 2 1 1030.89 5.07 5223.56 1030.89 0 0
8 2 1 314.17 5.75 1806.4775 1345.06 0 0
9 2 1 239.56 6.3 1508.24 1509.228 0 0
10 2 2 -554.46 0 0 954.768 0 0
11 2 1 826.24 5.884 4861.5961 1781.008 0 0
Expected output
ID|GrpID|InOut| LoadFt | LoadCostft| LoadCost | RunFt | RunCost| AvgRunCostFt
1 1 1 4549.00 0.99 4503.51 4549.00 4503.51 0.99
2 1 1 1523.22 1.29 1964.9538 6072.22 6468.4638 1.0653
3 1 2 -2491.73 1.0653 -2490.6647 3580.49 3977.7991 1.111
4 1 2 -96.00 1.111 -106.656 3484.49 3871.1431 1.111
5 1 1 8471.68 1.41 11945.0688 11956.17 15816.2119 1.3228
6 1 2 -369.00 1.3228 -488.1132 11468.0568 15328.0987 1.3366
7 2 1 1030.89 5.07 5223.56 1030.89 5223.56 5.067
8 2 1 314.17 5.75 1806.4775 1345.06 7030.0375 5.2266
9 2 1 239.56 6.3 1508.24 1509.228 8539.2655 5.658
10 2 2 -554.46 5.658 -3137.1346 954.768 5402.1309 5.658
11 2 1 826.24 5.884 4861.5961 1781.008 10263.727 5.7629
The first record of a group would be considered the opening balance. Inventory going into the yard have the ID of 1 and out of the yard are 2's. Load footage going into the yard always has a load cost per foot and I can calculate the the running total of footage. The first record of a group is easy to calculate the run cost and run cost per foot. The next record becomes a little more difficult to calculate. I need to move the average of run cost per foot forward to the load cost per foot when something is going out of the yard and then calculate the run cost and average run cost per foot again. Hopefully this makes sense to somebody and we can automate some of these calculations. Thanks for any help.
Here's an Oracle example I found;
SQL> select order_id
2 , volume
3 , price
4 , total_vol
5 , total_costs
6 , unit_costs
7 from ( select order_id
8 , volume
9 , price
10 , volume total_vol
11 , 0.0 total_costs
12 , 0.0 unit_costs
13 , row_number() over (order by order_id) rn
14 from costs
15 order by order_id
16 )
17 model
18 dimension by (order_id)
19 measures (volume, price, total_vol, total_costs, unit_costs)
20 rules iterate (4)
21 ( total_vol[any] = volume[cv()] + nvl(total_vol[cv()-1],0.0)
22 , total_costs[any]
23 = case SIGN(volume[cv()])
24 when -1 then total_vol[cv()] * nvl(unit_costs[cv()-1],0.0)
25 else volume[cv()] * price[cv()] + nvl(total_costs[cv()-1],0.0)
26 end
27 , unit_costs[any] = total_costs[cv()] / total_vol[cv()]
28 )
29 order by order_id
30 /
ORDER_ID VOLUME PRICE TOTAL_VOL TOTAL_COSTS UNIT_COSTS
---------- ---------- ---------- ---------- ----------- ----------
1 1000 100 1000 100000 100
2 -500 110 500 50000 100
3 1500 80 2000 170000 85
4 -100 150 1900 161500 85
5 -600 110 1300 110500 85
6 700 105 2000 184000 92
6 rows selected.
Let me say first off three things:
This is certainly not the best way to do it. There is a rule saying that if you need a while-loop, then you are most probably doing something wrong.
I suspect there is some calculation errors in your original "Expected output", please check the calculations since my calculated values are different according to your formulas.
This question could also be seen as a gimme teh codez type of question, but since you asked a decently formed question with some follow-up research, my answer is below. (So no upvoting since this is help for a specific case)
Now onto the solution:
I attempted to use my initial hint of the LAG statement in a nicely formed single update statement, but since you can only use a windowed function (aka LAG) inside a select or order by clause, that will not work.
What the code below does in short:
It calculates the various calculated fields for each record when they can be calculated and with the appropriate functions, updates the table and then moves onto the next record.
Please see comments in the code for additional information.
TempTable is a demo table (visible in the linked SQLFiddle).
Please read this answer for information about decimal(19, 4)
-- Our state and running variables
DECLARE #curId INT = 0,
#curGrpId INT,
#prevId INT = 0,
#prevGrpId INT = 0,
#LoadCostFt DECIMAL(19, 4),
#RunFt DECIMAL(19, 4),
#RunCost DECIMAL(19, 4)
WHILE EXISTS (SELECT 1
FROM TempTable
WHERE DoneFlag = 0) -- DoneFlag is a bit column I added to the table for calculation purposes, could also be called "IsCalced"
BEGIN
SELECT top 1 -- top 1 here to get the next row based on the ID column
#prevId = #curId,
#curId = tmp.ID,
#curGrpId = Grpid
FROM TempTable tmp
WHERE tmp.DoneFlag = 0
ORDER BY tmp.GrpID, tmp.ID -- order by to ensure that we get everything from one GrpID first
-- Calculate the LoadCostFt.
-- It is either predetermined (if InOut = 1) or derived from the previous record's AvgRunCostFt (if InOut = 2)
SELECT #LoadCostFt = CASE
WHEN tmp.INOUT = 2
THEN (lag(tmp.AvgRunCostFt, 1, 0.0) OVER (partition BY GrpId ORDER BY ID))
ELSE tmp.LoadCostFt
END
FROM TempTable tmp
WHERE tmp.ID IN (#curId, #prevId)
AND tmp.GrpID = #curGrpId
-- Calculate the LoadCost
UPDATE TempTable
SET LoadCost = LoadFt * #LoadCostFt
WHERE Id = #curId
-- Calculate the current RunFt and RunCost based on the current LoadFt and LoadCost plus the previous row's RunFt and RunCost
SELECT #RunFt = (LoadFt + (lag(RunFt, 1, 0) OVER (partition BY GrpId ORDER BY ID))),
#RunCost = (LoadCost + (lag(RunCost, 1, 0) OVER (partition BY GrpId ORDER BY ID)))
FROM TempTable tmp
WHERE tmp.ID IN (#curId, #prevId)
AND tmp.GrpID = #curGrpId
-- Set all our values, including the AvgRunCostFt calc
UPDATE TempTable
SET RunFt = #RunFt,
RunCost = #RunCost,
LoadCostFt = #LoadCostFt,
AvgRunCostFt = #RunCost / #RunFt,
doneflag = 1
WHERE ID = #curId
END
SELECT ID, GrpID, InOut, LoadFt, RunFt, LoadCost,
RunCost, LoadCostFt, AvgRunCostFt
FROM TempTable
ORDER BY GrpID, Id
The output with your sample data and a SQLFiddle demonstrating how it all works:
ID GrpID InOut LoadFt RunFt LoadCost RunCost LoadCostFt AvgRunCostFt
1 1 1 4549 4549 4503.51 4503.51 0.99 0.99
2 1 1 1523.22 6072.22 1964.9538 6468.4638 1.29 1.0653
3 1 2 -2491.73 3580.49 -2654.44 3814.0238 1.0653 1.0652
4 1 2 -96 3484.49 -102.2592 3711.7646 1.0652 1.0652
5 1 1 8471.68 11956.17 11945.0688 15656.8334 1.41 1.3095
6 1 2 -369 11587.17 -483.2055 15173.6279 1.3095 1.3095
7 2 1 1030.89 1030.89 5226.6123 5226.6123 5.07 5.07
8 2 1 314.17 1345.06 1806.4775 7033.0898 5.75 5.2288
9 2 1 239.56 1584.62 1509.228 8542.3178 6.3 5.3908
10 2 2 -554.46 1030.16 -2988.983 5553.3348 5.3908 5.3907
11 2 1 826.24 1856.4 4861.5962 10414.931 5.884 5.6103
If you are unclear about parts of the code, I can update with additional explanations.