I have a table on hand:
order_id, order_status (2=completed, 1=canceled), client_id, fee
I need to find how many client completed 1 order, and how many client completed more than 1 order.
I tried case as below but no clue:
select client_id, count(case order_status when '2' then 'completed' end) as cnt_completed, sum(fee)
from order
where cnt_completed = 1;
Are there any straightforward ways to get the numbers of clients who completed 1 order and numbers of clients who completed more than 1 order, along with the sum of fee for each group? Thanks.
Use two levels of aggregation to get this broken out by the number of orders:
select cnt, count(*), sum(sum_fee)
from (select client_id, count(*) as cnt, sum(fee) as sum_fee
from order o
where order_status = '2'
group by client_id
) o
group by cnt;
For just two rows, you can use:
select (case when cnt = 1 then 1 else 2 end), count(*), sum(sum_fee)
from (select client_id, count(*) as cnt, sum(fee) as sum_fee
from order o
where order_status = '2'
group by client_id
) o
group by (case when cnt = 1 then 1 else 2 end);
Related
I have a code as below where I want to count number of first purchases for a given period of time. I have a column in my sales table where if the buyer is not a first time buyer, then is_first_purchase = 0
For example:
buyer_id = 456391 is already an existing buyer who made purchases on 2 different dates.
Hence is_first_purchase column will show as 0 as per below.
If i do a count() on is_first_purchase for this buyer_id = 456391 then it should return 0 instead of 2.
My query is as follows:
with first_purchases as
(select *,
case when is_first_purchase = 1 then 'Yes' else 'No' end as first_purchase
from sales)
select
count(case when first_purchase = 'Yes' then 1 else 0 end) as no_of_first_purchases
from first_purchases
where buyer_id = 456391
and date_id between '2021-02-01' and '2021-03-01'
order by 1 desc;
It returned the below which is not an intended output
Appreciate if someone can help explain how to exclude is_first_purchase = 0 from the count, thanks.
Because COUNT function count when the value isn't NULL (include 0), if you don't want to count, need to let CASE WHEN return NULL
There are two ways you can count as your expectation, one is SUM other is COUNT but remove the part of else 0
SUM(case when first_purchase = 'Yes' then 1 else 0 end) as no_of_first_purchases
COUNT(case when first_purchase = 'Yes' then 1 end) as no_of_first_purchases
From your question, I would combine CTE and main query as below
select
COUNT(case when is_first_purchase = 1 then 1 end) as no_of_first_purchases
from sales
where buyer_id = 456391
and date_id between '2021-02-01' and '2021-03-01'
order by 1 desc;
I think that you are using COUNT() when you want SUM().
with first_purchases as
(select *,
case when is_first_purchase = 1 then 'Yes' else 'No' end as first_purchase
from sales)
select
SUM(case when first_purchase = 'Yes' then 1 else 0 end) as no_of_first_purchases
from first_purchases
where buyer_id = 456391
and date_id between '2021-02-01' and '2021-03-01'
order by 1 desc;
You could simplify your query as:
SELECT COUNT(*) AS
FROM sales no_of_first_purchases
WHERE is_first_purchase = 1
AND buyer_id = 456391
AND date_id BETWEEN '2021-02-01' AND '2021-03-01'
ORDER BY 1 DESC;
It is better to avoid the use of functions like IF and CASE when it can be done with WHERE.
The simplest approach for Trino (f.k.a. Presto SQL) is to use an aggregate with a filter:
count(name) FILTER (WHERE first_purchase = 'Yes') AS no_of_first_purchases
I have below table
Table: Sales
I Need to write Oracle SQL query to identify new customers each day,
when a customer (Customer-X) do 1 new transaction on a specific date(Date-X), customer is considered as new customer only for this date(Date-X), if same customer (Customer-X) makes other transactions after that(Date-Y), then this customer is not considered new customer for any date bigger than Date-X
this customer is only considered new Customer on Date-X and counted 1 time only
I also need to generate the summary result
which is the count of new customers for each day
You can use window functions:
select s.*,
(case when row_number() over (partition by user_id order by s_date) = 1
then 'Yes' else 'No'
end) as new_customer
from sales s;
You can then aggregate for the summary:
select s_date, sum(case when new_customer = 'Yes' then 1 else 0 end)
from (select s.*,
(case when row_number() over (partition by user_id order by s_date) = 1
then 'Yes' else 'No'
end) as new_customer
from sales s
) s
group by s_date
I'm trying to figure out if what I'm trying to do is possible. Instead of resorting to multiple queries on a table, I wanted to group the records by business date and id then group by the id and select one date for a field and another date for the other field.
SELECT
*
{AMOUNT FROM DATE}
{AMOUNT FROM OTHER DATE}
FROM (
SELECT
date,
id,
SUM(amount) AS amount
FROM
table
GROUP BY id, date
AS subquery
GROUP BY id
It seems that you're looking to do a pivot query. I usually use cross tabs for this. Based on the query you posted, it could look like:
SELECT
id,
SUM(CASE WHEN date = '20190901' THEN amount ELSE 0 END) AmountFromSept01,
SUM(CASE WHEN date = '20191001' THEN amount ELSE 0 END) AmountFromOct01
FROM (
SELECT
date,
id,
SUM(amount) AS amount
FROM
table
GROUP BY id, date
)AS subquery
GROUP BY id;
You could also use a CTE.
WITH CTE AS(
SELECT
date,
id,
SUM(amount) AS amount
FROM
table
GROUP BY id, date
)
SELECT
id,
SUM(CASE WHEN date = '20190901' THEN amount ELSE 0 END) AmountFromSept01,
SUM(CASE WHEN date = '20191001' THEN amount ELSE 0 END) AmountFromOct01
FROM CTE
GROUP BY id;
Or even be a rebel and do the operation directly.
SELECT
id,
SUM(CASE WHEN date = '20190901' THEN amount ELSE 0 END) AmountFromSept01,
SUM(CASE WHEN date = '20191001' THEN amount ELSE 0 END) AmountFromOct01
FROM CTE
GROUP BY id;
However, some people have tested for performance and found that pre-aggregating can improve performance.
If I understand you correctly, then you're just trying to pivot, but only with two particular dates:
select id,
date1 = sum(iif(date = '2000-01-01', amount, null)),
date2 = sum(iif(date = '2000-01-02', amount, null))
from [table]
group by id
I'm trying to make a promo grouping using one promo_code field in a month where there's a chance that a single customer_ID would have more than one transaction and could have two different promo code
SELECT customer_id AS buyer,
CASE
WHEN COUNT(DISTINCT flag_promo) = 2 THEN 'Mixed'
WHEN COUNT(DISTINCT flag_promo) = 1 AND flag_promo = 1 THEN 'Promo'
WHEN COUNT(DISTINCT flag_promo) = 1 AND flag_promo = 0 THEN 'Organic'
END AS promo_group
FROM TABLE
WHERE DATE BETWEEN '2019-04-01' AND '2019-04-30'
GROUP BY 1
ORDER BY 2
It gave me an error message :
SELECT list expression references column flag_promo which is neither grouped nor aggregated at [4:41]
Below is for BigQuery Standard SQL
#standardSQL
SELECT customer_id AS buyer,
CASE
WHEN COUNT(DISTINCT flag_promo) > 1 THEN 'Mixed'
WHEN ANY_VALUE(flag_promo) = 1 THEN 'Promo'
WHEN ANY_VALUE(flag_promo) = 2 THEN 'Organic'
END AS promo_group
FROM `project.dataset.table`
WHERE DATE BETWEEN '2019-04-01' AND '2019-04-30'
GROUP BY 1
ORDER BY 2
This is the query I think you intended to do:
SELECT
customer_id AS buyer,
CASE WHEN COUNT(DISTINCT flag_promo) = 2 THEN 'Mixed'
WHEN COUNT(DISTINCT flag_promo) = 1 AND MIN(flag_promo) = 1 THEN 'Promo'
WHEN COUNT(DISTINCT flag_promo) = 1 AND MIN(flag_promo) = 2 THEN 'Organic'
END AS promo_group
FROM TABLE
WHERE
DATE BETWEEN '2019-04-01' AND '2019-04-30'
GROUP BY 1
ORDER BY 2;
This assumes that a flag_promo value of 1 means Promo and a value of 2 means Organic. If not, then we can easily edit the above query.
I am working on Hive and am facing an issue with rolling counts. The sample data I am working on is as shown below:
and the output I am expecting is as shown below:
I tried using the following query but it is not returning the rolling count:
select event_dt,status, count(distinct account) from
(select *, row_number() over (partition by account order by event_dt
desc)
as rnum from table.A
where event_dt between '2018-05-02' and '2018-05-04') x where rnum =1
group by event_dt, status;
Please help me with this if some one has solved a similar issue.
You seem to just want conditional aggregation:
select event_dt,
sum(case when status = 'Registered' then 1 else 0 end) as registered,
sum(case when status = 'active_acct' then 1 else 0 end) as active_acct,
sum(case when status = 'suspended' then 1 else 0 end) as suspended,
sum(case when status = 'reactive' then 1 else 0 end) as reactive
from table.A
group by event_dt
order by event_dt;
EDIT:
This is a tricky problem. The solution I've come up with does a cross-product of dates and users and then calculates the most recent status as of each date.
So:
select a.event_dt,
sum(case when aa.status = 'Registered' then 1 else 0 end) as registered,
sum(case when aa.status = 'active_acct' then 1 else 0 end) as active_acct,
sum(case when aa.status = 'suspended' then 1 else 0 end) as suspended,
sum(case when aa.status = 'reactive' then 1 else 0 end) as reactive
from (select d.event_dt, ac.account, a.status,
max(case when a.status is not null then a.timestamp end) over (partition by ac.account order by d.event_dt) as last_status_timestamp
from (select distinct event_dt from table.A) d cross join
(select distinct account from table.A) ac left join
(select a.*,
row_number() over (partition by account, event_dt order by timestamp desc) as seqnum
from table.A a
) a
on a.event_dt = d.event_dt and
a.account = ac.account and
a.seqnum = 1 -- get the last one on the date
) a left join
table.A aa
on aa.timestamp = a.last_status_timestamp and
aa.account = a.account
group by d.event_dt
order by d.event_dt;
What this is doing is creating a derived table with rows for all accounts and dates. This has the status on certain days, but not all days.
The cumulative max for last_status_timestamp calculates the most recent timestamp that has a valid status. This is then joined back to the table to get the status on that date. Voila! This is the status used for the conditional aggregation.
The cumulative max and join is a work-around because Hive does not (yet?) support the ignore nulls option in lag().