This is my table and data
id | owner | buyer
1 1 3
2 2 2
3 1 2
I want the result to be like this
user | totals
2 3
1 2
3 1
User field means owner and buyer.
Hope you all are understand.
Thanks ~
You can do this using union all and group by:
select user, count(*)
from ((select owner as user from t
) union all
(select buyer from t
)
) ob
group by user
order by user;
Related
I'm working with a medical claim table in pyspark and I want to return only userid's that have at least 2 claim_ids. My table looks something like this:
claim_id | userid | diagnosis_type | claim_type
__________________________________________________
1 1 C100 M
2 1 C100a M
3 2 D50 F
5 3 G200 M
6 3 C100 M
7 4 C100a M
8 4 D50 F
9 4 A25 F
From this example, I would want to return userid's 1, 3, and 4 only. Currently I'm building a temp table to count all of the distinct instances of the claim_ids
create table temp.claim_count as
select distinct userid, count(distinct claim_id) as claims
from medical_claims
group by userid
and then pulling from this table when the number of claim_id >1
select distinct userid
from medical_claims
where userid (
select distinct userid
from temp.claim_count
where claims>1)
Is there a better / more efficient way of doing this?
If you want only the ids, then use group by:
select userid, count(*) as claims
from medical_claims
group by userid
having count(*) > 1;
If you want the original rows, then use window functions:
select mc.*
from (select mc.*, count(*) over (partition by userid) as num_claims
from medical_claims mc
) mc
where num_claims > 1;
I have 2 tables A and B. The columns names are similar in both the tables. The columns are
1. fees
2. user_id
I want to get the sum of fees from both tables where user_id = 1
For eg:
Table A:
id user_id fees
1 1 10
2 2 11
3 1 5
Table B:
id user_id fees
1 1 15
2 2 10
3 1 20
I need the result as below:
user_id fees
1 50
2 21
Please help me with the query
Try this:
select user_id, sum(fees) from (
select user_id, fees from Table_A
union all
select user_id, fees from Table_B
) as A
group by user_id
I have a list of total store visits for a customer for a month. The customer has a home store but can visit other stores. Like the table below:
MemberId | HomeStoreId | VisitedStoreId | Month | Visits
1 5 5 1 5
1 5 3 1 2
1 5 2 1 1
1 5 4 1 7
I want my select statement to give the number of visits to the home store against each store for that member for that month. Like the below:
MemberId | HomeStoreId | VisitedStoreId | Month | Visits | HomeStoreVisits
1 5 5 1 5 5
1 5 3 1 2 5
1 5 2 1 1 5
1 5 4 1 7 5
I've looked at a SUM with CASE statements inside and OVER with PARTITION but I can't seem to work it out.
Thanks
I would use window functions:
select t.*,
sum(case when homestoreid = visitedstoreid then visits end) over
(partition by memberid, month) as homestorevisits
from t;
SELECT MemberID,HomestoreID,visitedstoreid,Month,visits, homestorevisits
FROM Table LEFT OUTER JOIN
(SELECT MemberID, Visits homestorevisits
FROM TABLE WHERE homestoreID =VisitedStoreId
)T ON T.MemberID = Table.MemberID
You can achieve this using a simple subquery.
SELECT MemberId, HomeStoreID, VisitedStoreID, Month, Visits,
(SELECT Visits FROM table t2
WHERE t2.MemberId = t1.MemberId
AND t2.HomeStoreId = t1.HomeStoreId
AND t2.Month = t1.Month
AND t2.VisitedStoreId = t2.HomeStoreId) AS HomeStoreVisits
FROM table t1
I have 2 tables named user and statistics
user table has 3 columns: id, name and category
statistics table has 3 columns: id, idUser (relational), cal
something like this:
user
Id name category
1 name1 1
2 name2 2
3 name3 3
statistics
Id idUser cal
1 1 1
2 1 1
3 1 1
4 2 1
5 2 1
How can I apply a query that sum the cal column by each category of users and give me something like this:
category totalcal
1 3
2 2
3 0
You want to do a left join to keep all the categories. The rest is just aggregation:
select u.category, coalesce(sum(s.cal), 0) as cal
from users u left join
statistics s
on u.id = s.idUser
group by u.category;
Use LEFT JOIN to get 0 sum for the category=3:
SELECT
user.category
,SUM(statistics.cal) AS totalcal
FROM
user
LEFT JOIN statistics ON statistics.idUser = user.Id
GROUP BY
user.category
Here SUM would return NULL for category=3. To get 0 instead of NULL you can use COALESCE(SUM(statistics.cal), 0).
For a university project I have to calculate a kpi based on the data of one table. The table stores data about baskets of a supermarket and the shopped line items and their product category. I have to calculate a number of all product categories of products which were bought in a specific store. So in tables it looks like this:
StoreId BasketID CategoryId
1 1 1
1 1 2
1 1 3
1 2 1
1 2 3
1 2 4
2 3 1
2 3 2
2 3 3
2 4 1
As a result of the query I want a table which counts the distinct product categories over all basket associated to a store.
Something like this:
StoreId Count(CategoryId)
1 4
2 3
If I do a not dynamic statement with hard values, it is working.
select basket_hash, store_id, count(DISTINCT retailer_category_id)
from promo.checkout_item
where store_id = 2
and basket_hash = 123
GROUP BY basket_hash, store_id;
But when I try to write it in a dynamic way, the sql calculates the amount per basket and adds the single amounts together.
select store_id, Count(DISTINCT retailer_category_id)
from promo.checkout_item
group by store_id;
But like this it isn't comparing the categories over all baskets associated to a store and I'm getting duplicates because a category can be in basket 1 and in basket 2.
Can somebody pls help?!
Thx!
As your expected result, Do you want following statement?
SELECT StoreId, COUNT(*)
FROM (
SELECT DISTINCT StoreId, CategoryId
FROM table_name
)
GROUP BY StoreId;
Please, replace "table_name" in statement by your table's name.
I'm not sure what is "dynamic way" meaning.
I'm confused by your requirements. This is what I suppose you mean:
with checkout_item (store_id, basket_hash, retailer_category_id) as (
values
(1,1,1),(1,1,2),(1,1,3),(1,2,1),(1,2,3),
(1,2,4),(2,3,1),(2,3,2),(2,3,3),(2,4,1)
)
select distinct store_id, basket_hash, store_cats, basket_cats
from (
select store_id, basket_hash,
max(store_cats) over (partition by store_id) as store_cats,
max(basket_cats) over (partition by basket_hash) as basket_cats
from (
select store_id, basket_hash,
dense_rank() over (
partition by store_id
order by retailer_category_id
) as store_cats,
dense_rank() over (
partition by basket_hash
order by retailer_category_id
) as basket_cats
from checkout_item
) s
) s
order by 1, 2
;
store_id | basket_hash | store_cats | basket_cats
----------+-------------+------------+-------------
1 | 1 | 4 | 3
1 | 2 | 4 | 3
2 | 3 | 3 | 3
2 | 4 | 3 | 1