Given this table containing buying information of customer_id "xxxxxx". How can we write a script that computes the column nb_bmw_cars that are to the number of rows where car_type is "BMW" BUT in the interval ] date - 365 days: date - 1 day [ for each date. (in sqlalchemy).
customer_id | date | car_type | nb_bmw_cars |
---|---|---|---|
xxxxxx | 2022-11-22 | BMW | - |
xxxxxx | 2023-08-24 | BMW | 1 |
xxxxxx | 2023-12-21 | BMW | 2 |
xxxxxx | 2023-12-21 | BMW | 2 |
xxxxxx | 2023-12-21 | MERCEDES | 2 |
xxxxxx | 2023-12-21 | BMW | 2 |
xxxxxx | 2023-12-21 | BMW | 2 |
xxxxxx | 2024-02-07 | BMW | 6 |
xxxxxx | 2024-02-07 | BMW | 6 |
xxxxxx | 2024-02-07 | BMW | 6 |
xxxxxx | 2024-10-31 | BMW | 9 |
xxxxxx | 2024-10-31 | BMW | 9 |
xxxxxx | 2024-10-31 | BMW | 9 |
Given this table containing buying information of customer_id "xxxxxx". How can we write a script that computes the column nb_bmw_cars that are to the number of rows where car_type is "BMW" BUT in the interval ] date - 365 days: date - 1 day [ for each date. (in sqlalchemy).
customer_id | date | car_type | nb_bmw_cars |
---|---|---|---|
xxxxxx | 2022-11-22 | BMW | - |
xxxxxx | 2023-08-24 | BMW | 1 |
xxxxxx | 2023-12-21 | BMW | 2 |
xxxxxx | 2023-12-21 | BMW | 2 |
xxxxxx | 2023-12-21 | MERCEDES | 2 |
xxxxxx | 2023-12-21 | BMW | 2 |
xxxxxx | 2023-12-21 | BMW | 2 |
xxxxxx | 2024-02-07 | BMW | 6 |
xxxxxx | 2024-02-07 | BMW | 6 |
xxxxxx | 2024-02-07 | BMW | 6 |
xxxxxx | 2024-10-31 | BMW | 9 |
xxxxxx | 2024-10-31 | BMW | 9 |
xxxxxx | 2024-10-31 | BMW | 9 |
To understand what is nb_bmw_cars, if you take for example "2024-10-31", it has 9 because there is 9 transactions of car_type BMW before the date (2024-10-31) .
Share Improve this question edited Jan 18 at 7:17 redj asked Jan 17 at 22:01 redjredj 901 silver badge7 bronze badges 4 |2 Answers
Reset to default 1For the date 2024-10-31
, nb_bmw_cars
should be 7 instead of 9(as mentioned in question) since there are dates(2022-11-22
,2023-08-24
) older than
(2024-10-31) -1
and (2024-10-31) - 365
days which is the criteria mentioned in question.Same holds true for other dates as well.
To calculate the logic you can just self join the table with itself, where a date is between 365 days and -1 and filter the car_type as BMW
In case you want to calculate for different customer ids, you would need to join based on customer id.I have commented it here as in the sample data given it is not required as all customer ids are same.
select_query = text("""
select t1.customer_id, t1.date, t1.car_type,
(select COUNT(*) FROM test AS t2
--where t2.customer_id = t1.customer_id
where t2.date between date(t1.date, '-365 day') and date(t1.date, '-1 day')
and t2.car_type = 'BMW'
) as nb_bmw_cars
from test AS t1
""")
result = session.execute(select_query).fetchall()
Output
xxxxxx | 2022-11-22 | BMW | 0
xxxxxx | 2023-08-24 | BMW | 1
xxxxxx | 2023-12-21 | BMW | 1
xxxxxx | 2023-12-21 | BMW | 1
xxxxxx | 2023-12-21 | MERCEDES | 1
xxxxxx | 2023-12-21 | BMW | 1
xxxxxx | 2023-12-21 | BMW | 1
xxxxxx | 2024-02-07 | BMW | 5
xxxxxx | 2024-02-07 | BMW | 5
xxxxxx | 2024-02-07 | BMW | 5
xxxxxx | 2024-10-31 | BMW | 7
xxxxxx | 2024-10-31 | BMW | 7
xxxxxx | 2024-10-31 | BMW | 7
It is not clear whether you want to update or select, in case you wanted update here is an equivalent
update = text("""update test
set nb_bmw_cars = (select COUNT(*) from test AS t2
where t2.customer_id = test.customer_id
and t2.car_type = 'BMW'
and t2.date BETWEEN DATE(test.date, '-365 day') AND DATE(test.date, '-1 day'))""")
Conceptually it is the same, but here is the Sqlalchemy ORM version of it.
Sample data
data = [
{'customer_id': 'xxxxxx', 'date': date(2022, 11, 22), 'car_type': 'BMW'},
{'customer_id': 'xxxxxx', 'date': date(2023, 8, 24), 'car_type': 'BMW'},
{'customer_id': 'xxxxxx', 'date': date(2023, 12, 21), 'car_type': 'BMW'},
{'customer_id': 'xxxxxx', 'date': date(2023, 12, 21), 'car_type': 'BMW'},
{'customer_id': 'xxxxxx', 'date': date(2023, 12, 21), 'car_type': 'MERCEDES'},
{'customer_id': 'xxxxxx', 'date': date(2023, 12, 21), 'car_type': 'BMW'},
{'customer_id': 'xxxxxx', 'date': date(2023, 12, 21), 'car_type': 'BMW'},
{'customer_id': 'xxxxxx', 'date': date(2024, 2, 7), 'car_type': 'BMW'},
{'customer_id': 'xxxxxx', 'date': date(2024, 2, 7), 'car_type': 'BMW'},
{'customer_id': 'xxxxxx', 'date': date(2024, 2, 7), 'car_type': 'BMW'},
{'customer_id': 'xxxxxx', 'date': date(2024, 10, 31), 'car_type': 'BMW'},
{'customer_id': 'xxxxxx', 'date': date(2024, 10, 31), 'car_type': 'BMW'},
{'customer_id': 'xxxxxx', 'date': date(2024, 10, 31), 'car_type': 'BMW'}
]
-- connections etc
for each_row in all_rows:
interval_start = each_row.date - timedelta(days=365)
interval_end = each_row.date - timedelta(days=1)
count = connection.execute(
select(func.count()).where(and_(
test.columns.customer_id ==
each_row.customer_id,test.columns.car_type ==
'BMW',test.columns.date.between(interval_start,
interval_end)))).scalar()
connection.execute(
test.update().where(and_(
test.columns.customer_id == each_row.customer_id,
test.columns.date == each_row.date
)).values(nb_bmw_cars=count) )
results = connection.execute(select(test.columns.customer_id, test.columns.date, test.columns.car_type, test.columns.nb_bmw_cars)).fetchall()
for each in results:
print(each)
Output
('xxxxxx', datetime.date(2022, 11, 22), 'BMW', 0)
('xxxxxx', datetime.date(2023, 8, 24), 'BMW', 1)
('xxxxxx', datetime.date(2023, 12, 21), 'BMW', 1)
('xxxxxx', datetime.date(2023, 12, 21), 'BMW', 1)
('xxxxxx', datetime.date(2023, 12, 21), 'MERCEDES', 1)
('xxxxxx', datetime.date(2023, 12, 21), 'BMW', 1)
('xxxxxx', datetime.date(2023, 12, 21), 'BMW', 1)
('xxxxxx', datetime.date(2024, 2, 7), 'BMW', 5)
('xxxxxx', datetime.date(2024, 2, 7), 'BMW', 5)
('xxxxxx', datetime.date(2024, 2, 7), 'BMW', 5)
('xxxxxx', datetime.date(2024, 10, 31), 'BMW', 7)
('xxxxxx', datetime.date(2024, 10, 31), 'BMW', 7)
('xxxxxx', datetime.date(2024, 10, 31), 'BMW', 7)
Thanks for your answers, I post this one here in sqlalchemy. It worked for me it is based on a self join like @samhita mentioned in the comment above.
table_cars_alias_1 = aliased(table_cars)
table_cars_alias_2 = aliased(table_cars)
select_query = (
select(
table_cars_alias_1.c.customer_id,
table_cars_alias_1.c.date,
table_cars_alias_1.c.type_car,
func.sum(
case(
(table_cars_alias_2.c.type_car == 'BMW', 1),
else_=0
)
).label("nb_cars_bmw"),
func.sum(case(
(table_cars_alias_2.c.type_car == 'MERCEDES', 1)
,else_=0 )
).label("nb_cars_mercedes")
).select_from(
table_cars_alias_1.join(
table_cars_alias_2,
and_(
table_cars_alias_1.c.customer_id == table_cars_alias_2.c.customer_id,
table_cars_alias_2.c.date.between(
func.date(table_cars_alias_1.c.date) - text("interval '365 days'"),
func.date(table_cars_alias_1.c.date) - text("interval '1 days'")
)
)
)
)
.group_by(
table_cars_alias_1.c.customer_id,
table_cars_alias_1.c.date,
table_cars_alias_1.c.type_car,
)
)
test_alias = aliased(test) query = select(test.c.customer_id, test.c.date, test.c.car_type, func.count().filter( and_( test_alias.c.date.between( test.c.date - timedelta(days=365), test.c.date - timedelta(days=1) ), test.c.car_type == 'BMW' ) ).label('nb_bmw_cars') ).outerjoin(test_alias, and_(test_alias.c.customer_id == test_alias.c.customer_id) ).group_by(test.c.customer_id, test.c.date, test.c.car_type)
– redj Commented Jan 18 at 9:39