So the question seems to be quite difficult I wonder if I could get some advice from here. I am trying to solve this with SQLite 3. So I have a data format of this.
customer | purchase date
1 | date 1
1 | date 2
1 | date 3
2 | date 4
2 | date 5
2 | date 6
2 | date 7
number of times the customer repeats is random.
so I just want to find whether customer 1's 1st and 2nd purchase date are fallen in between a specific time period. repeat for other customers. only need to consider 1st and 2nd dates.
Any help would be appreciated!
We can try using ROW_NUMBER here:
WITH cte AS (
SELECT *, ROW_NUMBER() OVER (PARTITION BY customer ORDER BY "purchase date") rn
FROM yourTable
)
SELECT
customer,
CAST(MAX(CASE WHEN rn = 2 THEN julianday("purchase date") END) -
MAX(CASE WHEN rn = 1 THEN julianday("purchase date") END) AS INTEGER) AS diff_in_days
FROM cte
GROUP BY
customer;
The idea here is to aggregate by customer and then take the date difference between the second and first purchase. ROW_NUMBER is used to find these first and second purchases, for each customer.
My SQL data set is like this;
Date firm_id
======================
2010-01 1
2010-01 2
2010-01 3
----------------------
2010-02 1
2010-02 2
----------------------
2010-03 1
2010-03 2
2010-03 3
----------------------
2010-04 1
2010-04 3
How can I create a variable, name firm_age, to represent age of firms existing continuously from the previous period? like this,
Date firm_id firm_age
=================================
2010-01 1 0
2010-01 2 0
2010-01 3 0
-----------------------------------
2010-02 1 1
2010-02 2 1
-----------------------------------
2010-03 1 2
2010-03 2 2
2010-03 3 0
-----------------------------------
2010-04 1 3
2010-04 3 1
Thank you
This is a use case for the PACK operator from "Time & Relational Theory", which is not supported, at least not directly, in SQL.
You are trying to find [for each given row of the table] the smallest month such that there does not exist any intervening month between that smallest month and the month of the given row such that the company of the given row did not exist at that intervening month. Given two months, assessing the [non-]existence of such an intervening month is relatively trivial, however, finding the smallest month that makes the condition true for all intervening months is another order (*). I wouldn't try to do this completely in plain SQL.
(*) which set of months are you going to SELECT that "smallest month" from ? You cannot rely on the fact that all months will be mentioned in your table as there is always the slight theoretical possibility that one particular month, no companies existed at all. (This possibility also breaks any attack on the problem based on window functions ans row_numbers.)
This is a gaps-and-islands problem. You want "islands" where the values are sequential. Then you want to enumerate them. You can use row_number() for this:
select t.*,
row_number() over (partition by firm_id, date - seqnum * interval '1 month'
order by date
) as firm_age
from (select t.*,
row_number() over (partition by firm_id order by date) as seqnum
from t
) t;
Note that date functions are not standard across databases. This makes some assumptions about the data representation, but the idea for the processing should work in almost any database.
Hello I'm stuck trying to calculate the difference in time between each transaction for each ID.
The data looks like
Customer_ID | Transaction_Time
1 00:30
1 00:35
1 00:37
1 00:38
2 00:20
2 00:21
2 00:23
I'm trying to get the result to look something like
Customer_ID | Time_diff
1 5
1 2
1 1
2 1
2 2
I would really appreciate any help.
Thanks
Most databases support the LAG() function. However, the date/time functions can depend on the database. Here is an example for SQL Server:
select t.*
from (select t.*,
datediff(second,
lag(transaction_time) over (partition by customer_id order by transaction_time),
transaction_time
) as diff
from t
) t
where diff is not null;
The logic would be similar in most databases, although the function for calculating the time difference varies.
I need to find out how many attempts it takes to achieve an outcome from a SQL table. For example My table contains CustomerID, Outcome, OutcomeType. The outcome I am looking for is Sale
So if I had this record:
CID Outcome OutcomeID Date
1 No Answer 0 01/01/2015 08:00:00
1 No Interest 0 02/01/2015 09:00:00
1 Sale 1 02/02/2015 10:00:00
1 Follow up 2 03/02/2015 10:00:00
I can see it took 2 attempts to get a sale. I need to do this for all the customers in a table which contains thousands of entries. They may have entries after the sale and I need to exclude these, they may also have additional sales after the first but I am only interested in the first sale.
i hope this is enough info,
many thanks in advance
Edit as requested, the outcome I would look for would be:
CID CountToOutcome
1 2
2 3
3 5
etc
You can do this with window functions and aggregation:
select cid,
min(case when Outcome = 'Sale' then seqnum end) - 1 as AttemptsBeforeSale
from (select t.*,
row_number() over (partition by cid order by date) as seqnum
from t
) t
group by cid;
Note: This provides the value for the first sale for each cid.
I was trying to aggregate a 7 days data for FY13 (starts on 10/1/2012 and ends on 9/30/2013) in SQL Server but so far no luck yet. Could someone please take a look. Below is my example data.
DATE BREAD MILK
10/1/12 1 3
10/2/12 2 4
10/3/12 2 3
10/4/12 0 4
10/5/12 4 0
10/6/12 2 1
10/7/12 1 3
10/8/12 2 4
10/9/12 2 3
10/10/12 0 4
10/11/12 4 0
10/12/12 2 1
10/13/12 2 1
So, my desired output would be like:
DATE BREAD MILK
10/1/12 1 3
10/2/12 2 4
10/3/12 2 3
10/4/12 0 4
10/5/12 4 0
10/6/12 2 1
Total 11 15
10/7/12 1 3
10/8/12 2 4
10/9/12 2 3
10/10/12 0 4
10/11/12 4 0
10/12/12 2 1
10/13/12 2 1
Total 13 16
--------through 9/30/2013
Please note, since FY13 starts on 10/1/2012 and ends on 9/30/2012, the first week of FY13 is 6 days instead of 7 days.
I am using SQL server 2008.
You could add a new computed column for the date values to group them by week and sum the other columns, something like this:
SELECT DATEPART(ww, DATEADD(d,-2,[DATE])) AS WEEK_NO,
SUM(Bread) AS Bread_Total, SUM(Milk) as Milk_Total
FROM YOUR_TABLE
GROUP BY DATEPART(ww, DATEADD(d,-2,[DATE]))
Note: I used DATEADD and subtracted 2 days to set the first day of the week to Monday based on your dates. You can modify this if required.
Use option with GROUP BY ROLLUP operator
SELECT CASE WHEN DATE IS NULL THEN 'Total' ELSE CONVERT(nvarchar(10), DATE, 101) END AS DATE,
SUM(BREAD) AS BREAD, SUM(MILK) AS MILK
FROM dbo.test54
GROUP BY ROLLUP(DATE),(DATENAME(week, DATE))
Demo on SQLFiddle
Result:
DATE BREAD MILK
10/01/2012 1 3
10/02/2012 2 4
10/03/2012 2 3
10/04/2012 0 4
10/05/2012 4 0
10/06/2012 2 1
Total 11 15
10/07/2012 1 3
10/08/2012 4 7
10/10/2012 0 4
10/11/2012 4 0
10/12/2012 2 1
10/13/2012 2 1
Total 13 16
You are looking for a rollup. In this case, you will need at least one more column to group by to do your rollup on, the easiest way to do that is to add a computed column that groups them into weeks by date.
Take a lookg at: Summarizing Data Using ROLLUP
Here is the general idea of how it could be done:
You need a derived column for each row to determine which fiscal week that record belongs to. In general you could subtract that record's date from 10/1, get the number of days that have elapsed, divide by 7, and floor the result.
Then you can GROUP BY that derived column and use the SUM aggregate function.
The biggest wrinkle is that 6 day week you start with. You may have to add some logic to make sure that the weeks start on Sunday or whatever day you use but this should get you started.
The WITH ROLLUP suggestions above can help; you'll need to save the data and transform it as you need.
The biggest thing you'll need to be able to do is identify your weeks properly. If you don't have those loaded into tables already so you can identify them, you can build them on the fly. Here's one way to do that:
CREATE TABLE #fy (fyear int, fstart datetime, fend datetime);
CREATE TABLE #fylist(fyyear int, fydate DATETIME, fyweek int);
INSERT INTO #fy
SELECT 2012, '2011-10-01', '2012-09-30'
UNION ALL
SELECT 2013, '2012-10-01', '2013-09-30';
INSERT INTO #fylist
( fyyear, fydate )
SELECT fyear, DATEADD(DAY, Number, DATEADD(DAY, -1, fy.fstart)) AS fydate
FROM Common.NUMBERS
CROSS APPLY (SELECT * FROM #fy WHERE fyear = 2013) fy
WHERE fy.fend >= DATEADD(DAY, Number, DATEADD(DAY, -1, fy.fstart));
WITH weekcalc AS
(
SELECT DISTINCT DATEPART(YEAR, fydate) yr, DATEPART(week, fydate) dt
FROM #fylist
),
ridcalc AS
(
SELECT
ROW_NUMBER() OVER (ORDER BY yr, dt) AS rid, yr, dt
FROM weekcalc
)
UPDATE #fylist
SET fyweek = rid
FROM #fylist
JOIN ridcalc
ON DATEPART(YEAR, fydate) = yr
AND DATEPART(week, fydate) = dt;
SELECT list.fyyear, list.fyweek, p.[date], COUNT(bread) AS Bread, COUNT(Milk) AS Milk
FROM products p
JOIN #fylist list
ON p.[date] = list.fydate
GROUP BY list.fyyear, list.fyweek, p.[date] WITH ROLLUP;
The Common.Numbers reference above is a simple numbers table that I use for this sort of thing (goes from 1 to 1M). You could also build that on the fly as needed.