How would I go about having a separate column that shows the prior week's value? For example, if Product A's value for 01/03/2021 was 100, I would like 01/10/2021 to show its date value as well as the 01/03/2021 value in a separate column.
Desired table below (for simplicity sake I added random numbers for the prior week values for 01/03 and 01/04):
Date
Product
Value
Prior Week Value
01/03/2021
Product A
100
50
01/04/2021
Product A
200
55
01/10/2021
Product A
600
100
01/11/2021
Product A
700
200
01/03/2021
Product B
250
40
01/04/2021
Product B
550
45
01/10/2021
Product B
460
250
01/11/2021
Product B
100
550
If you want exactly 7 days before, you can use window functions with a range specification:
select t.*,
max(value) over (partition by product
order by date
range between '7 day' preceding and '7 day' preceding
) as value_prev_week
from t;
Related
I want to do a window function (like the SUM() OVER() function), but there are two catches:
I want to consider the last 3 months on my moving sum, but the number of rows are not consistent. Some months have 3 entries, others may have 2, 4, 5, etc;
There is also a "group" column, and the moving sum should sum only the amounts of the same group.
In summary, a have a table that has the following structure:
id
date
group
amount
1
2022-01
group A
1100
2
2022-01
group D
2500
3
2022-02
group A
3000
4
2022-02
group B
1000
5
2022-02
group C
2500
6
2022-03
group A
2000
7
2022-04
group C
1000
8
2022-05
group A
1500
9
2022-05
group D
2000
10
2022-06
group B
1000
So, I want to add a moving sum column, containing the sum the amount for each group for the last 3 months. The sum should not reset every 3 months, but should consider only the previous values from the 3 months prior, and of the same group.
The end result should look like:
id
date
group
amount
moving_sum_three_months
1
2022-01
group A
1100
1100
2
2022-01
group D
2500
2500
3
2022-02
group A
3000
4100
4
2022-02
group B
1000
1000
5
2022-02
group C
2500
2500
6
2022-03
group A
2000
6100
7
2022-04
group C
1000
3500
8
2022-05
group A
1500
3500
9
2022-05
group D
2000
2000
10
2022-06
group B
1200
1200
The best example to see how the sum work in this example is line 8.
It considers only lines 8 and 6 for the sum, because they are the only one that meet the criteria;
Line 1 and 3 do not meet the criteria, because they are more than 3 months old from line 8 date;
All the other lines are not from group A, so they are also excluded from the sum.
Any ideias? Thanks in advance for the help!
Use SUM() as a window function partitioning the window by group in RANGE mode. Set the frame to go back 3 months prior the current record using INTERVAL '3 months', e.g.
SELECT *, SUM(amount) OVER w AS moving_sum_three_months
FROM t
WINDOW w AS (PARTITION BY "group" ORDER BY "date"
RANGE BETWEEN INTERVAL '3 months' PRECEDING AND CURRENT ROW)
ORDER BY id
Demo: db<>fiddle
SO I have a few data with same date on google spreadsheet
Date Number
01/03/2021 3000
01/03/2021 2000
01/03/2021 1000
02/03/2021 500
05/03/2021 700
I would like to sum the number and sort it to the 01/03/2021 cell in another sheet
but how to sum all data in March without manually doing it for each day?
Date Number
01/03/2021 6000
02/03/2021 500
05/03/2021 700
thanks!!!
A basic GROUP BY query should yield the result you want:
SELECT Date, SUM(Number) AS Number
FROM yourTable
GROUP BY Date;
So my doubt is in sql. I am looking to find the total revenue of a parent account for the last 12 months.
The data will look something like this
revenue
name
month
year
10000
abc
201001
2010-01-12
10000
abc
201402
2014-02-14
2000
abc
201404
2014-04-12
3000
abc
201406
2014-06-30
30000
def
201301
2013-01-14
6000
def
201304
2013-04-12
9000
def
201407
2013-07-19
And the output should be something like this
revenue
name
month
year
Running Sum
10000
abc
201001
2010-01-12
10000
10000
abc
201402
2014-02-14
10000
2000
abc
201404
2014-04-12
12000
3000
abc
201406
2014-06-30
15000
30000
def
201301
2013-01-14
30000
6000
def
201304
2013-04-12
36000
9000
def
201407
2013-07-19
45000
I have tried using using windowing function something like this and the logic that I need
select revenue, name, date, month,
sum(revenue) over (partition by name order by month rows between '12 months' preceding AND CURRENT ROW )
from table
but the above command gives a syntax error
Redshift does not support intervals in the window frame specification.
So, convert to a number. A convenient one in this case is the number of months since some point in time:
select revenue, name, date, month,
sum(revenue) over (partition by name
order by datediff(month, '1900-01-01', month)
range between 12 preceding and current row
)
from table;
I will note that your logic adds up data from 13 months, not 12. I suspect you want between 11 preceding and current row.
You can use rows between if you have data for all months:
sum(revenue) over (partition by name
order by datediff(month, '1900-01-01', month)
rows between 12 preceding and current row
)
See table A. There are number of sales per date. The dates are not continuous.
I want table B where it gives the sales moves per the previous date in the dataset.
I am trying to do it in SQL but get stuck. I can do an individual day on day difference by entering the date but I want one where I don't need to enter the dates manually
A
Date Sales
01/01/2019 100
05/01/2019 200
12/01/2019 50
25/01/2019 25
31/01/2019 200
B
Date DOD Move
01/01/2019 -
05/01/2019 +100
12/01/2019 -150
25/01/2019 -25
31/01/2019 +175
Use lag():
select t.*,
(sales - lag(sales) over (order by date)) as dod_move
from t;
I have a table which has the monthly sales values for each of the items. I need last 3 months average sales value next to the current month sales for each item.
Need to perform this operation in hive.
The sample input table looks like below
Item_ID Sales Month
A 4295 Dec-2018
A 245 Nov-2018
A 1337 Oct-2018
A 3290 Sep-2018
A 2000 Aug-2018
B 856 Dec-2018
B 1694 Nov-2018
B 4286 Oct-2018
B 2780 Sep-2018
B 3100 Aug-2018
The result table should look like this
Item_ID Sales_Current_Month Month Sales_Last_3_months_average
A 4295 Dec-2018 1624
A 245 Nov-2018 2209
B 856 Dec-2018 2920
B 1694 Nov-2018 3388.67
Assuming there is no missing months data, you can use avg window function to do this.
select t.*
,avg(sales) over(partition by item_id order by month rows between 3 preceding and 1 preceding) as avg_sales_prev_3_months
from tbl t
If month column is in a format different from yyyyMM, use an appropriate conversion so the ordering works as expected.