Flask-SQLAlchemy query for count

You can do this with a CTE (common table expression) for a select that produces buyer ids together with their request counts, so

buyer_id | request_count
:------- | :------------
1        | 5
2        | 3
3        | 1
4        | 1

You can filter here on the counts having to be greater than 0 to be listed.

You can then join the buyers table against that to produce:

buyer_id | buyer_name | buyer_email      | request_count
:------- | :--------- | :--------------- | :------------
1        | foo        | [email protected]  | 5
2        | bar        | [email protected]  | 3
3        | baz        | [email protected]  | 1
4        | spam       | [email protected] | 1

but because we are using a CTE, you can also query the CTE for the lowest count value. In the above example, that's 1, and you can add a WHERE clause to the joined buyer-with-cte-counts query to filter the results down to only rows where the request_count value is equal to that minimum number.

The SQL query for this is

WITH request_counts AS (
    SELECT request.buyer_id AS buyer_id, count(request.id) AS request_count
    FROM request GROUP BY request.buyer_id
    HAVING count(request.id) > ?
)
SELECT buyer.*
FROM buyer
JOIN request_counts ON buyer.id = request_counts.buyer_id
WHERE request_counts.request_count = (
    SELECT min(request_counts.request_count)
    FROM request_counts
)

The WITH request_counts AS (...) defines a CTE, and it is that part that would produce the first table with buyer_id and request_count. The request_count table is then joined with request and the WHERE clause does the filtering on the min(request_counts.request_count) value.

Translating the above to Flask-SQLAlchemy code:

request_count = db.func.count(Request.id).label("request_count")
cte = (
    db.select([Request.buyer_id.label("buyer_id"), request_count])
    .group_by(Request.buyer_id)
    .having(request_count > 0)
    .cte('request_counts')
)
min_request_count = db.select([db.func.min(cte.c.request_count)]).as_scalar()
buyers_with_least_requests = Buyer.query.join(
    cte, Buyer.id == cte.c.buyer_id
).filter(cte.c.request_count == min_request_count).all()

Demo:

>>> __ = db.session.bulk_insert_mappings(
...     Buyer, [{"name": n} for n in ("foo", "bar", "baz", "spam", "no requests")]
... )
>>> buyers = Buyer.query.order_by(Buyer.id).all()
>>> requests = [
...     Request(buyer_id=b.id)
...     for b in [*([buyers[0]] * 3), *([buyers[1]] * 5), *[buyers[2], buyers[3]]]
... ]
>>> __ = db.session.add_all(requests)
>>> request_count = db.func.count(Request.id).label("request_count")
>>> cte = (
...     db.select([Request.buyer_id.label("buyer_id"), request_count])
...     .group_by(Request.buyer_id)
...     .having(request_count > 0)
...     .cte("request_counts")
... )
>>> buyers_w_counts = Buyer.query.join(cte, cte.c.buyer_id == Buyer.id)
>>> for buyer, count in buyers_w_counts.add_column(cte.c.request_count):
...     # print out buyer and request count for this demo
...     print(buyer, count, sep=": ")
<Buyer foo>: 3
<Buyer bar>: 5
<Buyer baz>: 1
<Buyer spam>: 1
>>> min_request_count = db.select([db.func.min(cte.c.request_count)]).as_scalar()
>>> buyers_w_counts.filter(cte.c.request_count == min_request_count).all()
[<Buyer baz>, <Buyer spam>]

I've also created a db<>fiddle here, containing the same queries, to play with.