PyQuickSQL

For a more thorough explanation see example.ipynb
To install: pip install qsql

How to use the query loader:

import quicksql as qq
queries = qq.LoadSQL('path/to/queries.sql')

Printing the queries should give you something like this:

LoadSQL('path/to/queries.sql')
Query Name: examplequery_1, Params: order_avg, num_orders
Query Name: examplequery_2, Params: sdate, edate, product_id

These are callable objects that return the string given the non-optional **params.
They are equivalent to an f-string + a lambda but loaded from an sql file and stored in the LoadSQL object.
print(str(queries)) will give you the raw SQL string.

How to use them:

print(queries.examplequery_2(
    product_id=10,
    sdate='1-10-2022',
    edate=qq.NoStr("DATE'4-11-2023'"),
    something_not_a_param='test'))

Unused variables: something_not_a_param in query examplequery_2

Above will be printed as a warning with invalid inclusions, no non-verbose option yet.

SELECT
  c.CustomerName,
  o.OrderDate,
  o.Status,
  (SELECT SUM(od.Quantity * od.UnitPrice) FROM OrderDetails od WHERE od.OrderID = o.OrderID) AS TotalValue
FROM
  Customers c
INNER JOIN Orders o ON c.CustomerID = o.CustomerID
WHERE
  o.OrderDate BETWEEN '1-10-2022' AND DATE'4-11-2023'
  AND EXISTS (SELECT 1 FROM OrderDetails od WHERE od.OrderID = o.OrderID AND od.ProductID = 10)
ORDER BY
  TotalValue DESC;

How to use the file cache:

This is very similar to functool's cache, with the main difference being that @qq.file_cache caches the asset to memory and to your system's default temporary directory. If the memory cache ever fails (eg a restarted kernel) it will load the asset from it's pickled file.

from random import randint
import quicksql as qq
@qq.file_cache()
def test_mem(size:int):
    return [randint(0,10) for _ in range(size)]

print(test_mem(8))

[10, 3, 4, 9, 2, 2, 4, 2]

print(test_mem(8))

[10, 3, 4, 9, 2, 2, 4, 2]

To clear the cache: qq.clear_cache()

For more examples and how to configure see example.ipynb