I have this sample table:
id date score
11 1/1/2017 14:32 25.34
4 1/2/2017 12:14 34.34
25 1/2/2017 18:08 37.15
4 3/2/2017 23:42 47.24
4 4/2/2017 23:42 54.12
25 7/3/2017 22:07 65.21
11 9/3/2017 21:02 74.6
25 10/3/2017 5:15 11.3
4 10/3/2017 7:11 22.45
My aim is to calculates the first(!) date (YYYY-MM-DD) on which an id's cumulative score has reached 100 (>=). For that, I've written the following code:
SELECT date(date),id, score,
sum(score) over (partition by id order by date(date) rows unbounded preceding) as cumulative_score
FROM test_q1
GROUP BY id, date, score
Order by id, date
It returns:
date id score cumulative_score
1/1/2017 11 25.34 25.34
9/3/2017 11 74.6 99.94
1/2/2017 4 34.34 34.34
3/2/2017 4 47.24 81.58
4/2/2017 4 54.12 135.7
10/3/2017 4 22.45 158.15
1/2/2017 25 37.15 37.15
7/3/2017 25 65.21 102.36
10/3/2017 25 11.3 113.66
I tried to add either WHERE cumulative_score >= 100 or HAVING cumulative score >= 100, but it returns_
ERROR: column "cumulative_score" does not exist
LINE 4: WHERE cumulative_score >= 100
^
SQL state: 42703
Character: 206
Anyone knows how to solve this?
Thanks
What I expect is:
date id score cumulative_score
4/2/2017 4 54.12 135.7
7/3/2017 25 65.21 102.36
And the output just id and date.
Try this:
with cumulative_sum AS (
SELECT id,date,sum(score) over( partition by id order by date) as sum from test_q1
),
above_100_score_rank AS (
SELECT *, rank() over (partition by id order by sum) AS rank
FROM cumulative_sum where sum > 100
)
SELECT * FROM above_100_score_rank WHERE rank= 1;
Related
I'm trying to write a query that calculates the number of days between the first and last score per id.
The data sample:
id date score
11 1/1/2017 25.34
4 1/2/2017 34.34
25 1/2/2017 15.78
4 3/2/2017 47.2
25 7/3/2017 65.21
11 9/3/2017 96.09
25 10/3/2017 11.3
4 10/3/2017 27.12
Which is far from what I need, but I'm really lost. Clueless to be honest. Any idea?
Thanks
Try this:
SELECT
customer_id,
date(last_score) - date(first_score) AS days_between_last_and_first_score,
total_score::float/(date(last_score) - date(first_score)) AS score_per_day
FROM
(
select customer_id,
MAX(date(purchase_date)) as last_score,
MIN(date(purchase_date)) as first_score,
SUM(score) AS total_score
FROM candidate_test_q1
group by customer_id
) AS sub_query
I am trying to use the built-in filter function in PostgreSQL to filter for a date range in order to sum only entries falling within this time-frame.
I cannot understand why the filter isn't being applied.
I am trying to filter for all product transactions that have a created_at date of the previous month (so in this case that were created in June 2017).
SELECT pt.created_at::date, pt.customer_id,
sum(pt.amount/100::double precision) filter (where (date_part('month', pt.created_at) =date_part('month', NOW() - interval '1 month') and
date_part('year', pt.created_at) = date_part('year', NOW()) ))
from
product_transactions pt
LEFT JOIN customers c
ON c.id= pt.customer_id
GROUP BY pt.created_at::date,pt.customer_id
Please find my expected results (sum of the amount for each day in the previous month - for each customer_id if an entry for that day exists) and the actual results I get from the query - below (using date_trunc).
Expected results:
created_at| customer_id | amount
2017-06-30 1 220.5
2017-06-28 15 34.8
2017-06-28 12 157
2017-06-28 48 105.6
2017-06-27 332 425.8
2017-06-25 1 58.0
2017-06-25 23 22.5
2017-06-21 14 88.9
2017-06-17 2 34.8
2017-06-12 87 250
2017-06-05 48 135.2
2017-06-05 12 95.7
2017-06-01 44 120
Results:
created_at| customer_id | amount
2017-06-30 1 220.5
2017-06-28 15 34.8
2017-06-28 12 157
2017-06-28 48 105.6
2017-06-27 332 425.8
2017-06-25 1 58.0
2017-06-25 23 22.5
2017-06-21 14 88.9
2017-06-17 2 34.8
2017-06-12 87 250
2017-06-05 48 135.2
2017-06-05 12 95.7
2017-06-01 44 120
2017-05-30 XX YYY
2017-05-25 XX YYY
2017-05-15 XX YYY
2017-04-30 XX YYY
2017-03-02 XX YYY
2016-11-02 XX YYY
The actual results give me the sum for all dates in the database, so no date time-frame is being applied in the query for a reason I cannot understand. I'm seeing dates that are both not for June 2017 and also from previous years.
Use date_trunc(..) function:
SELECT pt.created_at::date, pt.customer_id, c.name,
sum(pt.amount/100::double precision) filter (where date_trunc('month', pt.created_at) = date_trunc('month', NOW() - interval '1 month'))
from
product_transactions pt
LEFT JOIN customers c
ON c.id= pt.customer_id
GROUP BY pt.created_at::date
I need to find users who have posted three times or more, three months in a row. I wrote this query:
select count(id), owneruserid, extract(month from creationdate) as postmonth from posts
group by owneruserid, postmonth
having count(id) >=3
order by owneruserid, postmonth
And I get this:
count owneruserid postmonth
36 -1 1
23 -1 2
45 -1 3
41 -1 4
18 -1 5
24 -1 6
31 -1 7
78 -1 8
83 -1 9
17 -1 10
88 -1 11
127 -1 12
3 6 11
3 7 12
4 8 1
8 8 12
4 12 4
3 12 5
3 22 2
4 22 4
(truncated)
Which is great. How can I query for users who posted three times or more, three months or more in a row? Thanks.
This is called the Islands and Gaps problem, specifically it's an Island problem with a date range. You should,
Fix this question up.
Flag it to be sent to dba.stackexchange.com
To solve this,
Create a pseudo column with a window that has 1 if the row preceding it does not correspond to the preceding mont
Create groups out of that with COUNT()
Check to make sure the count(*) for the group is greater than or equal to three.
Query,
SELECT l.id, creationdaterange, count(*)
FROM (
SELECT t.id,
t.creationdate,
count(range_reset) OVER (PARTITION BY t.id ORDER BY creationdate) AS creationdaterange
FROM (
SELECT id,
creationdate,
CASE
WHEN date_trunc('month',creationdate::date)::date - interval '1 month' = date_trunc('month',lag(creationdate))::date OVER (PARTITION BY id ORDER BY creationdate)
THEN 1
END AS range_reset
FROM post
ORDER BY id, creationdate
) AS t;
) AS l
GROUP BY t.id, creationdaterange
HAVING count(*) >= 3;
I have a table that has data of user_id and the timestamp they joined.
If I need to display the data month-wise I could just use:
select
count(user_id),
date_trunc('month',(to_timestamp(users.timestamp))::timestamp)::date
from
users
group by 2
The date_trunc code allows to use 'second', 'day', 'week' etc. Hence I could get data grouped by such periods.
How do I get data grouped by "n-day" period say 45 days ?
Basically I need to display number users per 45 day period.
Any suggestion or guidance appreciated!
Currently I get:
Date Users
2015-03-01 47
2015-04-01 72
2015-05-01 123
2015-06-01 132
2015-07-01 136
2015-08-01 166
2015-09-01 129
2015-10-01 189
I would like the data to come in 45 days interval. Something like :-
Date Users
2015-03-01 85
2015-04-15 157
2015-05-30 192
2015-07-14 229
2015-08-28 210
2015-10-12 294
UPDATE:
I used the following to get the output, but one problem remains. I'm getting values that are offset.
with
new_window as (
select
generate_series as cohort
, lag(generate_series, 1) over () as cohort_lag
from
(
select
*
from
generate_series('2015-03-01'::date, '2016-01-01', '45 day')
)
t
)
select
--cohort
cohort_lag -- This worked. !!!
, count(*)
from
new_window
join users on
user_timestamp <= cohort
and user_timestamp > cohort_lag
group by 1
order by 1
But the output I am getting is:
Date Users
2015-04-15 85
2015-05-30 157
2015-07-14 193
2015-08-28 225
2015-10-12 210
Basically The users displayed at 2015-03-01 should be the users between 2015-03-01 and 2015-04-15 and so on.
But I seem to be getting values of users upto a date. ie: upto 2015-04-15 users 85. which is not the results I want.
Any help here ?
Try this query :
SELECT to_char(i::date,'YYYY-MM-DD') as date, 0 as users
FROM generate_series('2015-03-01', '2015-11-30','45 day'::interval) as i;
OUTPUT :
date users
2015-03-01 0
2015-04-15 0
2015-05-30 0
2015-07-14 0
2015-08-28 0
2015-10-12 0
2015-11-26 0
This looks like a hot mess, and it might be better wrapped in a function where you could use some variables, but would something like this work?
with number_of_intervals as (
select
min (timestamp)::date as first_date,
ceiling (extract (days from max (timestamp) - min (timestamp)) / 45)::int as num
from users
),
intervals as (
select
generate_series(0, num - 1, 1) int_start,
generate_series(1, num, 1) int_end
from number_of_intervals
),
date_spans as (
select
n.first_date + 45 * i.int_start as interval_start,
n.first_date + 45 * i.int_end as interval_end
from
number_of_intervals n
cross join intervals i
)
select
d.interval_start, count (*) as user_count
from
users u
join date_spans d on
u.timestamp >= d.interval_start and
u.timestamp < d.interval_end
group by
d.interval_start
order by
d.interval_start
With this sample data:
User Id timestamp derived range count
1 3/1/2015 3/1-4/15
2 3/26/2015 "
3 4/4/2015 "
4 4/6/2015 " (4)
5 5/6/2015 4/16-5/30
6 5/19/2015 " (2)
7 6/16/2015 5/31-7/14
8 6/27/2015 "
9 7/9/2015 " (3)
10 7/15/2015 7/15-8/28
11 8/8/2015 "
12 8/9/2015 "
13 8/22/2015 "
14 8/27/2015 " (5)
Here is the output:
2015-03-01 4
2015-04-15 2
2015-05-30 3
2015-07-14 5
I've got a table with a tracking of a plant's equipment installation.
Here is a sample:
ID Name Date Percentage
1 GT-001 2011-01-08 30
2 GT-002 2011-01-11 40
3 GT-003 2011-02-02 30
4 GT-001 2011-02-03 50
5 GT-003 2011-02-15 50
6 GT-004 2011-02-15 30
7 GT-002 2011-02-15 60
8 GT-001 2011-02-20 60
9 GT-003 2011-03-01 60
10 GT-004 2011-03-05 50
11 GT-001 2011-03-10 70
12 GT-004 2011-03-15 60
And the corresponding script:
CREATE TABLE [dbo].[SampleTable](
[ID] [int] NOT NULL,
[Name] [nvarchar](50) NULL,
[Date] [date] NULL,
[Percentage] [int] NULL) ON [PRIMARY]
GO
--Populate the table with values
INSERT INTO [dbo].[SampleTable] VALUES
('1', 'GT-001', '2011-01-08', '30'),
('2', 'GT-002', '2011-01-11', '40'),
('3', 'GT-003', '2011-02-02', '30'),
('4', 'GT-001', '2011-02-03', '50'),
('5', 'GT-003', '2011-02-15', '50'),
('6', 'GT-004', '2011-02-15', '30'),
('7', 'GT-002', '2011-02-15', '60'),
('8', 'GT-001', '2011-02-20', '60'),
('9', 'GT-003', '2011-03-01', '60'),
('10', 'GT-004', '2011-03-05', '50'),
('11', 'GT-001', '2011-03-10', '70'),
('12', 'GT-004', '2011-03-15', '60');
GO
What i need is to create a chart with Date on the X and Average Percentage on the Y. Average Percentage is an average percentage of all equipment by that particular date starting from the beggining of the installation process (MIN(Fields!Date.Value, "EquipmentDataset"))
Having no luck in implementing this using SSRS only, i decided to create a more complicated dataset for it using T-SQL.
I guess that it is nessesary to add a calculated column named 'AveragePercentage' that should store an average percentage on that date, calculating only the most latest equipment percentage values in a range between the beggining of the installation process (MIN(Date)) and the current row's date. Smells like a recursion, but i'm newbie to T-SQL....))
Here is the desired output
ID Name Date Percentage Average
1 GT-001 2011-01-08 30 30
2 GT-002 2011-01-11 40 35
3 GT-003 2011-02-02 30 33
4 GT-001 2011-02-03 50 40
5 GT-003 2011-02-15 50 48
6 GT-004 2011-02-15 30 48
7 GT-002 2011-02-15 60 48
8 GT-001 2011-02-20 60 50
9 GT-003 2011-03-01 60 53
10 GT-004 2011-03-05 50 58
11 GT-001 2011-03-10 70 60
12 GT-004 2011-03-15 60 63
What do you think?
I'll be very appreciated for any help.
You could use cross apply with row_number to find the latest value for each machine. An additional subquery is required because you cannot use row_number in the where clause directly. Here's the query:
select t1.id
, t1.Name
, t1.Date
, t1.Percentage
, avg(1.0*last_per_machine.percentage)
from SampleTable t1
outer apply
(
select *
from (
select row_number() over (partition by Name order by id desc)
as rn
, *
from SampleTable t2
where t2.date <= t1.date
) as numbered
where rn = 1
) as last_per_machine
group by
t1.id
, t1.Name
, t1.Date
, t1.Percentage
Working example on SE Data.