Ask questions about your data.

Lause compiles English sentences into programs that answer questions over CSV data.

Custom questions over your own data · By invitation

01 / Orders

Which orders are shippable?

Orders

3 editable facts

Order	Paid	Packed	Digital
O1	Yes	Yes	Unknown
O2	Unknown	Unknown	Yes
O3	Unknown	Unknown	Unknown

Checked: yes. Unchecked: unknown.

orders.csv · source data

Lause reads one fact per row. Unknown facts have no row.

thing,kind
O1,order
O1,paid
O1,packed
O2,order
O2,digital
O3,order

Which orders are shippable?

Standing bounded

Of the 3 orders, shippable: O1 (by sentence 2, and orders.csv lines 2, 3 and 4) and O2 (by sentence 3, and orders.csv lines 5 and 6). The document does not say of O3. None is known not shippable.

Sentences, rows and receipt

question: Which orders are shippable?
answer: Of the 3 orders, shippable: O1 (by sentence 2, and orders.csv lines 2, 3 and 4) and O2 (by sentence 3, and orders.csv lines 5 and 6). The document does not say of O3. None is known not shippable.
standing: bounded
basis: document sha256:7b4414ff…; orders.csv: 6 rows, sha256:e20919cb…; sha256:a0975899…: “orders” is the plural of “order”.; sha256:e831481a…: If an order is paid and it is packed, it is shippable.; sha256:ef1b98e3…: If an order is digital, it is shippable.; reader: sha256:dbd3a49d…; orders.csv: true; ; orders.csv: thing,kind; sentence 1: “orders” is the plural of “order”.; sentence 2: If an order is paid and it is packed, it is shippable.; sentence 3: If an order is digital, it is shippable.; data: 6 rows told from 1 file; the program closed and checked: 32 atoms, the kernel accepted
receipt: sha256:ce544e87…

sha256:ce544e871ec8a765efb71585c413de1a8f0e157e0fe2349fd6a426788f71d8f1

Is O1 shippable?

Standing established

Yes. O1 is shippable, by sentence 2, and orders.csv lines 2, 3 and 4.

Sentences, rows and receipt

question: Is O1 shippable?
answer: Yes. O1 is shippable, by sentence 2, and orders.csv lines 2, 3 and 4.
standing: established
basis: document sha256:7b4414ff…; orders.csv: 6 rows, sha256:e20919cb…; sha256:a0975899…: “orders” is the plural of “order”.; sha256:e831481a…: If an order is paid and it is packed, it is shippable.; sha256:ef1b98e3…: If an order is digital, it is shippable.; reader: sha256:dbd3a49d…; orders.csv: true; ; orders.csv: thing,kind; sentence 1: “orders” is the plural of “order”.; sentence 2: If an order is paid and it is packed, it is shippable.; sentence 3: If an order is digital, it is shippable.; data: 6 rows told from 1 file; the program closed and checked: 32 atoms, the kernel accepted
receipt: sha256:ce544e87…

sha256:ce544e871ec8a765efb71585c413de1a8f0e157e0fe2349fd6a426788f71d8f1

02 / The same question

Formula, query, script, sentences

Lause

policy.lause

“orders” is the plural of “order”.
If an order is paid and it is packed, it is shippable.
If an order is digital, it is shippable.

Spreadsheet

Excel 365 · A1:B7

=LET(
  id,A2:A7,
  kind,B2:B7,
  orders,UNIQUE(FILTER(id,kind="order")),
  FILTER(orders,
    (COUNTIFS(id,orders,kind,"digital")
    +COUNTIFS(id,orders,kind,"paid")
    *COUNTIFS(id,orders,kind,"packed"))>0,
    "")
)

SQL

SQLite

SELECT thing
FROM orders
GROUP BY thing
HAVING MAX(kind = 'order') = 1
   AND (
     MAX(kind = 'digital') = 1
     OR (
       MAX(kind = 'paid') = 1
       AND MAX(kind = 'packed') = 1
     )
   )
ORDER BY thing;

Python

Standard library

import csv
from collections import defaultdict

kinds = defaultdict(set)
with open("orders.csv", newline="") as file:
    for row in csv.DictReader(file):
        kinds[row["thing"]].add(row["kind"])

shippable = sorted(
    thing for thing, facts in kinds.items()
    if "order" in facts and (
        "digital" in facts
        or {"paid", "packed"} <= facts
    )
)
print("\n".join(shippable))

Each returns the orders known to be shippable; other orders remain unknown.

03 / Custom questions

Your data.

For people who maintain policies, and engineers who work with their data.

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