Here is the output. No problem here. Exactly what I want. But I added the DISTINCT ID to remove duplicates and that works in each grouped month.
MN | CNT
====================
1 | 1
10 | 2
11 | 5
12 | 5
SELECT EXTRACT(MONTH FROM TRUNC(HDATE)) as MN, COUNT(DISTINCT ID) as CNT
FROM Schema.TRAVEL
WHERE (ARR = '2' OR ARR = '3')
AND
HDATE BETWEEN to_date('2015-10-01', 'yyyy-mm-dd') AND to_date('2016-09-30', 'yyyy-mm-dd')
GROUP BY EXTRACT(MONTH FROM TRUNC(HDATE));
But I can still possibly have duplicates that span more than each month. So if I have a record in October and another in November with the same ID - I want to only count this once - that is my issue
So over the course of a year or any time period - an ID only gets counted once...but I still need to maintain the monthly groupings and output...
??
In other words, you want to count each id in the first month where it appears.
SELECT EXTRACT(MONTH FROM TRUNC(HDATE)) as MN, COUNT(DISTINCT ID) as CNT
FROM (SELECT id, MIN(HDATE) as HDATE
FROM Schema.TRAVEL t
WHERE ARR IN '2', '3') AND
HDATE BETWEEN DATE '2015-10-01' AND DATE '2016-09-30'
GROUP BY id
) t
GROUP BY EXTRACT(MONTH FROM TRUNC(HDATE));
Note: If an id appears before '2015-10-01', this will still count the id in the first month it appears after that date. If you don't want such an id counted at all, move the HDATE comparison to the outer query.
Related
I am trying to optimize the below query to help fetch all customers in the last three months who have a monthly order frequency +4 for the past three months.
Customer ID
Feb
Mar
Apr
0001
4
5
6
0002
3
2
4
0003
4
2
3
In the above table, the customer with Customer ID 0001 should only be picked, as he consistently has 4 or more orders in a month.
Below is a query I have written, which pulls all customers with an average purchase frequency of 4 in the last 90 days, but not considering there is a consistent purchase of 4 or more last three months.
Query:
SELECT distinct lines.customer_id Customer_ID, (COUNT(lines.order_id)/90) PurchaseFrequency
from fct_customer_order_lines lines
LEFT JOIN product_table product
ON lines.entity_id= product.entity_id
AND lines.vendor_id= product.vendor_id
WHERE LOWER(product.country_code)= "IN"
AND lines.date >= DATE_SUB(CURRENT_DATE() , INTERVAL 90 DAY )
AND lines.date < CURRENT_DATE()
GROUP BY Customer_ID
HAVING PurchaseFrequency >=4;
I tried to use window functions, however not sure if it needs to be used in this case.
I would sum the orders per month instead of computing the avg and then retrieve those who have that sum greater than 4 in the last three months.
Also I think you should select your interval using "month(CURRENT_DATE()) - 3" instead of using a window of 90 days. Of course if needed you should handle the case of when current_date is jan-feb-mar and in that case go back to oct-nov-dec of the previous year.
I'm not familiar with Google BigQuery so I can't write your query but I hope this helps.
So I've found the solution to this using WITH operator as below:
WITH filtered_orders AS (
select
distinct customer_id ID,
extract(MONTH from date) Order_Month,
count(order_id) CountofOrders
from customer_order_lines` lines
where EXTRACT(YEAR FROM date) = 2022 AND EXTRACT(MONTH FROM date) IN (2,3,4)
group by ID, Order_Month
having CountofOrders>=4)
select distinct ID
from filtered_orders
group by ID
having count(Order_Month) =3;
Hope this helps!
An option could be first count the orders by month and then filter users which have purchases on all months above your threshold:
WITH ORDERS_BY_MONTH AS (
SELECT
DATE_TRUNC(lines.date, MONTH) PurchaseMonth,
lines.customer_id Customer_ID,
COUNT(lines.order_id) PurchaseFrequency
FROM fct_customer_order_lines lines
LEFT JOIN product_table product
ON lines.entity_id= product.entity_id
AND lines.vendor_id= product.vendor_id
WHERE LOWER(product.country_code)= "IN"
AND lines.date >= DATE_SUB(CURRENT_DATE() , INTERVAL 90 DAY )
AND lines.date < CURRENT_DATE()
GROUP BY PurchaseMonth, Customer_ID
)
SELECT
Customer_ID,
AVG(PurchaseFrequency) AvgPurchaseFrequency
FROM ORDERS_BY_MONTH
GROUP BY Customer_ID
HAVING COUNT(1) = COUNTIF(PurchaseFrequency >= 4)
This is what my table looks like:
NOTE: Don't worry about the BMI field being empty in some rows. We assume that each row is a reading. I have omitted some columns for privacy reasons.
I want to get a count of the number of active customers per month. A customer is active if they have at least 18 readings in total (1 reading per day for 18 days in a given month). How do I write this SQL query? Assume the table name is 'cust'. I'm using SQL Server. Any help is appreciated.
Presumably a patient is a customer in your world. If so, you can use two levels of aggregation:
select yyyy, mm, count(*)
from (select year(createdat) as yyyy, month(createdat) as mm,
patient_id,
count(distinct convert(date, createdat)) as num_days
from t
group by year(createdat), month(createdat), patient_id
) ymp
where num_days >= 18
group by yyyy, mm;
You need to group by patient and the month, then group again by just the month
SELECT
mth,
COUNT(*) NumPatients
FROM (
SELECT
EOMONTH(c.createdat) mth
FROM cust c
GROUP BY EOMONTH(c.createdat), c.patient_id
HAVING COUNT(*) >= 18
-- for distinct days you could change it to:
-- HAVING COUNT(DISTINCT CAST(c.createdat AS date)) >= 18
) c
GROUP BY mth;
i have the following code,
SELECT
years_month_count.day_date,
years_month_count.year_date,
years_month_count.month_date,
years_month_count.no_of_customers_day,
sum(years_month_count.no_of_customers_day) OVER (PARTITION BY year_date ORDER BY day_date) AS no_of_customers_ytd
FROM (
SELECT
DATE(date) as day_date,
DATE_PART('year',date) as year_date,
DATE_PART('month',date) as month_date,
count(prepare_first_buyer.person_id) as no_of_customers_day
FROM (
SELECT
DATE(bestelldatum),
person_id,
ROW_NUMBER() OVER (PARTITION BY person_id ORDER BY person_id)
FROM ani.bestellung
) prepare_first_buyer
WHERE row_number=1
GROUP BY DATE(date), DATE_PART('year',date),DATE_PART('month',date)
ORDER BY DATE(date), DATE_PART('year',date),DATE_PART('month',date)
) years_month_count
the Output looks like this:
day_date
year_date
month_date
no_of_customers_day
no_of_Customers_ytd
2017-04-04
2017
4
6
6
2017-04-05
2017
4
4
10
...
...
...
...
...
...
...
...
...
...
and so on.
The no_of_customers_ytd will be set to zero at the beginning of every new year (1.January).
But what i need is to set it to zero on a special date, lets say 1.June every year.
So i need a sum from everything between the 1.June till 30.March for every Year.
Thx for the help.
step-by-step demo:db<>fiddle
SELECT
*,
SUM(value) OVER (PARTITION BY -- 4
date_part('year', -- 3
the_date - interval '5 months' -- 2
)
)
FROM t
WHERE date_part('month', the_date)::int NOT BETWEEN 4 AND 5 -- 1
Filter all dates you are not requiring. In your example all dates with months 4 and 5
Shift your date range start to the beginning of the year. In your example you have to shift: year-06-01 to year-01-01, so you need to subtract 5 months. Because your date range never exceeds a year, all your relevant data now has the same year, which makes a great group criterion
Extract the year part to use it as group/partition criterion
Do your calculation on this criterion
I'm pretty new to SQL and have this problem:
I have a filled table with a date column and other not interesting columns.
date | name | name2
2015-03-20 | peter | pan
2015-03-20 | john | wick
2015-03-18 | harry | potter
What im doing right now is counting everything for a date
select date, count(*)
from testtable
where date >= current date - 10 days
group by date
what i want to do now is counting the resulting lines and only returning them if there are less then 10 resulting lines.
What i tried so far is surrounding the whole query with a temp table and the counting everything which gives me the number of resulting lines (yeah)
with temp_count (date, counter) as
(
select date, count(*)
from testtable
where date >= current date - 10 days
group by date
)
select count(*)
from temp_count
What is still missing the check if the number is smaller then 10.
I was searching in this Forum and came across some "having" structs to use, but that forced me to use a "group by", which i can't.
I was thinking about something like this :
with temp_count (date, counter) as
(
select date, count(*)
from testtable
where date >= current date - 10 days
group by date
)
select *
from temp_count
having count(*) < 10
maybe im too tired to think of an easy solution, but i can't solve this so far
Edit: A picture for clarification since my english is horrible
http://imgur.com/1O6zwoh
I want to see the 2 columned results ONLY IF there are less then 10 rows overall
I think you just need to move your having clause to the inner query so that it is paired with the GROUP BY:
with temp_count (date, counter) as
(
select date, count(*)
from testtable
where date >= current date - 10 days
group by date
having count(*) < 10
)
select *
from temp_count
If what you want is to know whether the total # of records (after grouping), are returned, then you could do this:
with temp_count (date, counter) as
(
select date, counter=count(*)
from testtable
where date >= current date - 10 days
group by date
)
select date, counter
from (
select date, counter, rseq=row_number() over (order by date)
from temp_count
) x
group by date, counter
having max(rseq) >= 10
This will return 0 rows if there are less than 10 total, and will deliver ALL the results if there are 10 or more (you can just get the first 10 rows if needed with this also).
In your temp_count table, you can filter results with the WHERE clause:
with temp_count (date, counter) as
(
select date, count(distinct date)
from testtable
where date >= current date - 10 days
group by date
)
select *
from temp_count
where counter < 10
Something like:
with t(dt, rn, cnt) as (
select dt, row_number() over (order by dt) as rn
, count(1) as cnt
from testtable
where dt >= current date - 10 days
group by dt
)
select dt, cnt
from t where 10 >= (select max(rn) from t);
will do what you want (I think)
Trying to get a basic table that shows retention from one month to the next. So if someone buys something last month and they do so the next month it gets counted.
month, num_transactions, repeat_transactions, retention
2012-02, 5, 2, 40%
2012-03, 10, 3, 30%
2012-04, 15, 8, 53%
So if everyone that bought last month bought again the following month you have 100%.
So far I can only calculate stuff manually. This gives me the rows that have been seen in both months:
select count(*) as num_repeat_buyers from
(select distinct
to_char(transaction.timestamp, 'YYYY-MM') as month,
auth_user.email
from
auth_user,
transaction
where
auth_user.id = transaction.buyer_id and
to_char(transaction.timestamp, 'YYYY-MM') = '2012-03'
) as table1,
(select distinct
to_char(transaction.timestamp, 'YYYY-MM') as month,
auth_user.email
from
auth_user,
transaction
where
auth_user.id = transaction.buyer_id and
to_char(transaction.timestamp, 'YYYY-MM') = '2012-04'
) as table2
where table1.email = table2.email
This is not right but I feel like I can use some of Postgres' windowing functions. Keep in mind the windowing functions don't let you specify WHERE clauses. You mostly have access to the previous rows and the preceding rows:
select month, count(*) as num_transactions, count(*) over (PARTITION BY month ORDER BY month)
from
(select distinct
to_char(transaction.timestamp, 'YYYY-MM') as month,
auth_user.email
from
auth_user,
transaction
where
auth_user.id = transaction.buyer_id
order by
month
) as transactions_by_month
group by
month
Given the following test table (which you should have provided):
CREATE TEMP TABLE transaction (buyer_id int, tstamp timestamp);
INSERT INTO transaction VALUES
(1,'2012-01-03 20:00')
,(1,'2012-01-05 20:00')
,(1,'2012-01-07 20:00') -- multiple transactions this month
,(1,'2012-02-03 20:00') -- next month
,(1,'2012-03-05 20:00') -- next month
,(2,'2012-01-07 20:00')
,(2,'2012-03-07 20:00') -- not next month
,(3,'2012-01-07 20:00') -- just once
,(4,'2012-02-07 20:00'); -- just once
Table auth_user is not relevant to the problem.
Using tstamp as column name since I don't use base types as identifiers.
I am going to use the window function lag() to identify repeated buyers. To keep it short I combine aggregate and window functions in one query level. Bear in mind that window functions are applied after aggregate functions.
WITH t AS (
SELECT buyer_id
,date_trunc('month', tstamp) AS month
,count(*) AS item_transactions
,lag(date_trunc('month', tstamp)) OVER (PARTITION BY buyer_id
ORDER BY date_trunc('month', tstamp))
= date_trunc('month', tstamp) - interval '1 month'
OR NULL AS repeat_transaction
FROM transaction
WHERE tstamp >= '2012-01-01'::date
AND tstamp < '2012-05-01'::date -- time range of interest.
GROUP BY 1, 2
)
SELECT month
,sum(item_transactions) AS num_trans
,count(*) AS num_buyers
,count(repeat_transaction) AS repeat_buyers
,round(
CASE WHEN sum(item_transactions) > 0
THEN count(repeat_transaction) / sum(item_transactions) * 100
ELSE 0
END, 2) AS buyer_retention
FROM t
GROUP BY 1
ORDER BY 1;
Result:
month | num_trans | num_buyers | repeat_buyers | buyer_retention_pct
---------+-----------+------------+---------------+--------------------
2012-01 | 5 | 3 | 0 | 0.00
2012-02 | 2 | 2 | 1 | 50.00
2012-03 | 2 | 2 | 1 | 50.00
I extended your question to provide for the difference between the number of transactions and the number of buyers.
The OR NULL for repeat_transaction serves to convert FALSE to NULL, so those values do not get counted by count() in the next step.
-> SQLfiddle.
This uses CASE and EXISTS to get repeated transactions:
SELECT
*,
CASE
WHEN num_transactions = 0
THEN 0
ELSE round(100.0 * repeat_transactions / num_transactions, 2)
END AS retention
FROM
(
SELECT
to_char(timestamp, 'YYYY-MM') AS month,
count(*) AS num_transactions,
sum(CASE
WHEN EXISTS (
SELECT 1
FROM transaction AS t
JOIN auth_user AS u
ON t.buyer_id = u.id
WHERE
date_trunc('month', transaction.timestamp)
+ interval '1 month'
= date_trunc('month', t.timestamp)
AND auth_user.email = u.email
)
THEN 1
ELSE 0
END) AS repeat_transactions
FROM
transaction
JOIN auth_user
ON transaction.buyer_id = auth_user.id
GROUP BY 1
) AS summary
ORDER BY 1;
EDIT: Changed from minus 1 month to plus 1 month after reading the question again. My understanding now is that if someone buy something in 2012-02, and then buy something again in 2012-03, then his or her transactions in 2012-02 are counted as retention for the month.