Right so I have a table such as this in PostgreSQL:
timestamp duration
2013-04-03 15:44:58 4
2013-04-03 15:56:12 2
2013-04-03 16:13:17 9
2013-04-03 16:16:30 3
2013-04-03 16:29:52 1
2013-04-03 16:38:25 1
2013-04-03 16:41:37 9
2013-04-03 16:44:49 1
2013-04-03 17:01:07 9
2013-04-03 17:07:48 1
2013-04-03 17:11:00 2
2013-04-03 17:11:16 2
2013-04-03 17:15:17 1
2013-04-03 17:16:53 4
2013-04-03 17:20:37 9
2013-04-03 17:20:53 3
2013-04-03 17:25:48 3
2013-04-03 17:29:26 1
2013-04-03 17:32:38 9
2013-04-03 17:36:55 4
And I would like to get the following output:
timestampwindowstart = 2013-04-03 15:44:58
duration count
1 0
2 1
3 0
4 1
9 0
timestampwindowstart = 2013-04-03 15:59:58
duration count
1 0
2 0
3 0
4 0
9 1
timestampwindowstart = 2013-04-03 16:14:58
duration count
1 1
2 0
3 1
4 0
9 0
timestampwindowstart = 2013-04-03 16:29:58
duration count
1 2
2 0
3 0
4 0
9 1
etc...
So basically it cycles through the timestamps in 15 minute windows and outputs the distinct duration values along with their frequency (count). The timestampwindowstart value is the earliest timestamp for the window (i.e timestampwindowfinish = timestampwindowstart + 15 minutes)
This is so I can then plot the 15 minute interval histograms...
I have tried reading up but it is a bit complicated for me to get my head around and I don't have much time...
Thanks for any help!
Quick and dirty way: http://sqlfiddle.com/#!1/bd2f6/21 I named my column tstamp instead of your timestamp
with t as (
select
generate_series(mitstamp,matstamp,'15 minutes') as int,
duration
from
(select min(tstamp) mitstamp, max(tstamp) as matstamp from tmp) a,
(select duration from tmp group by duration) b
)
select
int as timestampwindowstart,
t.duration,
count(tmp.duration)
from
t
left join tmp on
(tmp.tstamp >= t.int and
tmp.tstamp < (t.int + interval '15 minutes') and
t.duration = tmp.duration)
group by
int,
t.duration
order by
int,
t.duration
Brief explanation:
Calculate minimum and maximum timestamp
Generate 15 minutes intervals between minimum and maximum
Cross join results with unique values of duration
Left join original data (left join is important, because this will keep all possible combination in output and there will be null where duration does not exists for given interval.
Aggregate data. count(null)=0
In case you have more tables and the algorithm should be applied on their union. Suppose we have three tables tmp1, tmp2, tmp3 all with columns tstamp and duration. The we can extend the previous solution:
with
tmpout as (
select * from tmp1 union all
select * from tmp2 union all
select * from tmp3
)
,t as (
select
generate_series(mitstamp,matstamp,'15 minutes') as int,
duration
from
(select min(tstamp) mitstamp, max(tstamp) as matstamp from tmpout) a,
(select duration from tmpout group by duration) b
)
select
int as timestampwindowstart,
t.duration,
count(tmp.duration)
from
t
left join tmpout on
(tmp.tstamp >= t.int and
tmp.tstamp < (t.int + interval '15 minutes') and
t.duration = tmp.duration)
group by
int,
t.duration
order by
int,
t.duration
You should really know with clause in PostgreSQL. It is invaluable concept for any data analysis in PostgreSQL.
Related
I am trying to find the best way to accomplish the following.
Get the beginning customer count, which carries from the previous day
Get New Customer count
Get the number of Customers who have not come in since the prior month
Get the number of Customers who have come back after lapsing
Get the number of total customers
The following example
Customer ID
Store ID
Date
Amount
1
1
1/2/22
1.00
2
2
1/2/22
2.00
1
1
2/2/22
1.00
3
2
3/2/22
1.00
2
2
3/2/22
1.00
1
1
3/2/22
1.00
1
1
4/2/22
1.00
4
1
4/2/22
1.00
2
2
4/2/22
1.00
The result would be
Date
Store
Beginning
New
Dropped
Returned
Total
1/2/22
1
0
1
0
0
1
1/2/22
2
0
1
0
0
1
2/2/22
1
1
0
0
0
1
2/2/22
2
1
0
1
0
0
3/2/22
1
1
0
0
0
1
3/2/22
2
0
1
0
1
2
4/2/22
1
1
1
0
0
2
4/2/22
2
2
0
1
0
1
I kind of have a query, but it's not getting the right results
WITH customerset AS (
SELECT
location_id,
date,
array_agg(DISTINCT customer_id ORDER BY customer_id ASC) AS customer_ids
FROM customer_orders
GROUP BY
location_id,
date
)
SELECT
cset.location_id,
cset.date,
array_length(cset2.customer_ids, 1) AS beginning,
array_length((past2.customer_ids - cset.customer_ids), 1) AS dropped,
array_length((cset.customer_ids - past2.customer_ids), 1) AS returned
FROM
(
SELECT
ords.location_id,
ords.date,
array_agg(DISTINCT ords.customer_id ORDER BY ords.customer_id ASC) AS customers_id
FROM customer_orders ords
GROUP BY
ords.location_id,
ords.date
) cset
JOIN
customerset cset2 ON cset.date - '1 month'::interval = cset2.date
AND cset2.location_id = cset.location_id
GROUP BY
cset.location_id,
cset.date,
cset2.customer_ids,
cset.customer_ids
ORDER BY
cset.date ASC
Is it possible to Average after every fixed interval and group by one column in MSSQL ?
Suppose I have a table A as under:
NAME Interval Data1 Data2
1 0.01 1 4
1 0.05 4 2
1 0.09 7 6
1 0.11 1 2
1 0.15 7 6
1 0.18 3 1
1 0.19 2 5
2 0.209 9 0
I want the Output to group by Name and run average every 10 counts.
So for expamle
Name - 1
Interval Start - 0
Interval End - 10
Data 1 Avg - 4 [(1 + 4 + 7) / 3]
Data 3 Avg - 4 [(4 + 2 + 6) / 3]
AND
Name - 1
Interval Start - 10
Interval End - 20
Data 1 Avg - 3.25 [(1 + 7 + 3 + 2) / 4]
Data 3 Avg - 3.50 [(2 + 6 + 1 + 5) / 4]
So I want the Ouput as below. The interval per "Name" column is different.
Name Interval-Start Interval-End DataAvg1 DataAvg2
1 0 10 4 4
1 10 20 3.25 3.50
2 0 10 0 0
2 10 20 0 0
2 20 30 9 0
I used the below query, but cant figure out logic per interval.
SELECT Name, Interval, AVG(Data1) AS Data1Avg, AVG(Data2) AS Data2Avg
FROM TableA
GROUP BY Name;
Can someone please help me with it.
using cursor and temp table
--drop table dbo.#result
--drop table dbo.#steps
CREATE TABLE dbo.#result
(
[Name] varchar(50),
[Interval-Start] float,
[Interval-End] float,
[DataAvg1] float,
[DataAvg2] float
)
CREATE TABLE dbo.#steps
(
[IntervalStart] float,
[IntervalEnd] float
)
declare #min int, #max int, #step float
DECLARE #Name varchar(50), #IntervalStart float, #IntervalEnd float;
set #min = 0
set #max = 1
set #step = 0.1
insert into #steps
select #min + Number * #step IntervalStart, #min + Number * #step + #step IntervalEnd
from master..spt_values
where type = 'P' and number between 0 and (#max - #min) / #step
DECLARE _cursor CURSOR FOR
SELECT [Name], [IntervalStart], [IntervalEnd] FROM
(select [Name] from [TableA] Group by [Name]) t
INNER JOIN #steps on 1=1
OPEN _cursor;
FETCH NEXT FROM _cursor
INTO #Name, #IntervalStart, #IntervalEnd;
WHILE ##FETCH_STATUS = 0
BEGIN
insert into dbo.#result
select #Name, #IntervalStart, #IntervalEnd, AVG(CAST(Data1 as FLOAT)), AVG(CAST(Data2 as FLOAT))
FROM [TableA]
where [NAME] = #Name and Interval between #IntervalStart and #IntervalEnd
FETCH NEXT FROM _cursor
INTO #Name, #IntervalStart, #IntervalEnd;
END
CLOSE _cursor;
DEALLOCATE _cursor;
select * from dbo.#result
i have this table in postgresql database
id name time
1 poi 2018-05-13 08:45:48.846887
2 poi 2018-05-13 08:11:04.671437
3 roik 2018-05-14 16:32:04.671437
4 ceil 2018-05-14 17:38:04.671437
5 verk 2018-05-14 19:46:04.671437
6 roik 2018-05-16 08:21:04.671437
7 poi 2018-05-16 11:00:04.671437
8 roik 2018-05-18 14:40:08.671437
9 roik 2018-05-18 17:21:09.671664
10 verk 2018-05-13 08:46:04.671437
11 sant ...
12 sant ...
13 dmk ...
14 roik ...
15 poi ...
... .... ...
I want to have such a table:
name 2018-5-1 2018-5-2 2018-5-3 2018-5-4 2018-5-5 2018-5-6 2018-5-7 2018-5-8 2018-5-9 2018-5-10 2018-5-11 2018-5-12 2018-5-13 2018-5-14 ...
poi 0 3 3 7
roik 0 4 2 1
verk 0 2 0 9
sant 1 0 8 2
dmk 0 3 ...
...
These numbers represent the number of repetitions of each person for each particular day
how can i do this??thank you in advance
This is not exactly what you want but very similar to. You just need to translate "date" colun values to a columns itself, but you can easily do it on client after you queried the dates you need.
https://www.db-fiddle.com/f/7r4AG9MV9zeZEjoU77fCfk/2
SELECT Test.name, SUM(CASE WHEN date(Test.time) = dates.date THEN 1 ELSE 0 END), dates.date FROM Test CROSS JOIN
(SELECT DISTINCT(date(time)) as date FROM Test) as dates
GROUP BY Test.name, dates.date
I have my data that looks like this:
user_id touchpoint_number days_difference
1 1 5
1 2 20
1 3 25
1 4 10
2 1 2
2 2 30
2 3 4
I would like to create one more column that would create a cumulative sum of the days_difference, partitioned by user_id, but would reset whenever the value reaches 30 and starts counting from 0. I have been trying to do it, but I couldn't figure it out how to do it in PostgreSQL, because it has to be recursive.
The outcome I would like to have would be something like:
user_id touchpoint_number days_difference cum_sum_upto30
1 1 5 5
1 2 20 25
1 3 25 0 --- new count all over again
1 4 10 10
2 1 2 2
2 2 30 0 --- new count all over again
2 3 4 4
Do you have any cool ideas how this could be done?
This should do what you want:
with cte as (
select t.a, t.b, t.c, t.c as sumc
from t
where b = 1
union all
select t.a, t.b, t.c,
(case when t.c + cte.sumc > 30 then 0 else t.c + cte.sumc end)
from t join
cte
on t.b = cte.b + 1 and t.a = cte.a
)
select *
from cte
order by a, b;
Here is a rextester.
Suppose I have data formatted in the following way (FYI, total row count is over 30K):
customer_id order_date order_rank
A 2017-02-19 1
A 2017-02-24 2
A 2017-03-31 3
A 2017-07-03 4
A 2017-08-10 5
B 2016-04-24 1
B 2016-04-30 2
C 2016-07-18 1
C 2016-09-01 2
C 2016-09-13 3
I need a 4th column, let's call it days_since_last_order which, in the case where order_rank = 1 then 0 else calculate the number of days since the previous order (with rank n-1).
So, the above would return:
customer_id order_date order_rank days_since_last_order
A 2017-02-19 1 0
A 2017-02-24 2 5
A 2017-03-31 3 35
A 2017-07-03 4 94
A 2017-08-10 5 38
B 2016-04-24 1 0
B 2016-04-30 2 6
C 2016-07-18 1 79
C 2016-09-01 2 45
C 2016-09-13 3 12
Is there an easier way to calculate the above with a window function (or similar) rather than join the entire dataset against itself (eg. on A.order_rank = B.order_rank - 1) and doing the calc?
Thanks!
use the lag window function
SELECT
customer_id
, order_date
, order_rank
, COALESCE(
DATE(order_date)
- DATE(LAG(order_date) OVER (PARTITION BY customer_id ORDER BY order_date))
, 0)
FROM <table_name>