I have a table transaction_details:
transaction_id
customer_id
item_id
item_number
transaction_dttm
7765
1
23
1
2022-01-15
1254
2
12
4
2022-02-03
3332
3
56
2
2022-02-15
7658
1
43
1
2022-03-01
7231
4
56
1
2022-01-15
7231
2
23
2
2022-01-29
I need to form a table of the following type customer_aggr:
customer_id
amount_spent_lm
top_item_lm
1
700
glasses
2
20000
notebook
3
100
cup
When calculating, it is necessary to take into account the current price at the time of the transaction (dict_item_prices). Customers who have not made purchases in the last month are not included in the final table. he last month is defined as the last 30 days at the time of the report creation.
There is also a table dict_item_prices:
item_id
item_name
item_price
valid_from_dt
valid_to_dt
23
phone 1
1000
2022-01-01
2022-12-31
12
notebook
5000
2022-01-02
2022-12-31
56
cup
50
2022-01-02
2022-12-31
43
glasses
700
2022-01-01
2022-12-31
Related
Let's assume I have a table like this:
id
date
value
1
2021-04-05
100
1
2021-04-04
50
1
2021-04-03
25
1
2021-04-02
5
2
2021-04-05
80
2
2021-04-04
20
2
2021-04-03
15
2
2021-04-02
10
I need to add another column that groups by id and calculates a day-over-day percent change from the value with the date before it. So for this example it would look like this:
id
date
value
pct_change
1
2021-04-05
100
100
1
2021-04-04
50
100
1
2021-04-03
25
400
1
2021-04-02
5
NaN
2
2021-04-05
80
300
2
2021-04-04
20
33.33
2
2021-04-03
15
50
2
2021-04-02
10
NaN
In python this would be easy, I could do something like this:
df['pct_change'] = df.groupby('id').value.pct_change() * 100
But if I wanted to do this in the Postgres database call, I'd suddenly implode with stupidity... does anybody know how to do this?
Maybe something like this?
SELECT
id,
date,
value,
(value - prev_value) / prev_value AS pct_change
FROM
(
SELECT
id,
date,
value,
LAG(value) OVER (PARTITION BY id ORDER BY date
ROWS BETWEEN 1 PRECEDING AND
CURRENT ROW) AS prev_value
FROM
your_table
)
ORDER BY date, id
I have little problem with counting cells with particular value in one row in MSSMS.
Table looks like
ID
Month
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
11
12
13
14
15
16
...
31
5000
1
null
null
1
1
null
1
1
null
null
2
2
2
2
2
null
null
3
3
3
3
3
null
...
1
I need to count how many cells in one row have value for example 1. In this case it would be 5.
Data represents worker shifts in a month. Be aware that there is a column named month (FK with values 1-12), i don't want to count that in a result.
Column ID is ALWAYS 4 digit number.
Possibility is to use count(case when) but in examples there are only two or three columns not 31. Statement will be very long. Is there any other option to count it?
Thanks for any advices.
I'm going to strongly suggest that you abandon your current table design, and instead store one day per month, per record, not column. That is, use this design:
ID | Date | Value
5000 | 2021-01-01 | NULL
5000 | 2021-01-02 | NULL
5000 | 2021-01-03 | 1
5000 | 2021-01-04 | 1
5000 | 2021-01-05 | NULL
...
5000 | 2021-01-31 | 5
Then use this query:
SELECT
ID,
CONVERT(varchar(7), Date, 120),
COUNT(CASE WHEN Value = 1 THEN 1 END) AS one_cnt
FROM yourTable
GROUP BY
ID,
CONVERT(varchar(7), Date, 120);
I've searched but so far don't find answer fits my situation.
How do you write select statement to selecting out duplicate records within the same table column and list them (so not group by it)??
example: to find duplicates for contract_id column and list them out
ID contract_id Sales1 Sales2
1 12345 100 200
2 54321 300 674
3 12345 343 435
4 09876 125 654
5 54321 374 233
6 22334 543 335
Result should look like this with order by contract_id as well:
ID contract_id Sales1 Sales2
1 12345 100 200
3 12345 343 435
2 54321 300 674
5 54321 374 233
You could use a subquery on the count >1
select * from my_table
where contract_id in (
select contract_id
from my_table
group by contract_id
having count(*) > 1
)
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 have two table 1000 of record given below.
My first table is USER table.
ID Name DateOfBirth
1 John 1980-11-20 00:00:00.000
2 Denial 1940-04-10 00:00:00.000
3 Binney 1995-12-25 00:00:00.000
4 Sara 1960-11-20 00:00:00.000
5 Poma 1980-11-20 00:00:00.000
6 Cameroon 1980-11-20 00:00:00.000
.....
.....
And my second table is CHANNEL_WATCH_DURATION_BY_USER
userid duration channelname
1 100 SAB
2 200 zee Tv
1 400 axn
2 0 star 1
3 800 star 2
3 700 star 3
4 200 star 4
.....
.....
I need to write the POSTGRES SQL Query to display different age groups contain duration with each channel.
under 18 20-30 age 30-40 age chaneel
10 40 100 star 1
20 0 200 star 2
30 79 0 zee
40 80 30 axn
.....
.....
SELECT
SUM(IF(DATEDIFF(NOW(),DateOfBirth)<18,1,0)) AS under18,
SUM(IF(DATEDIFF(NOW(),DateOfBirth) BETWEEN 20 AND 30,1,0)) as 20_to_30_age,
SUM(IF(DATEDIFF(NOW(),DateOfBirth)BETWEEN 30 AND 40,1,0)) as 30_to_40_age,
channelname as chaneel from
USER a,CHANNEL_WATCH_DURATION_BY_USER b where a.ID=b.USERID GROUP BY channelname