# Summarise the whole table Shape / table A variant of [Summarise by group](https://meridian.online/operations/summarise-by-group). Reduce the entire table to a single summary row, without grouping keys. Page: ## Status Runs as a SQL step. arcform carries it as a `sql:` step today. ## The DuckDB step summarise the whole table into one row It reads the `orders` and `customers` tables the proofs share. Create them first: ```sql CREATE TABLE orders AS SELECT * FROM (VALUES (1, 'Alice Ng', 'north', DATE '2026-01-03', 120.00, 'widget', 'a|b', '{"channel":"web","items":[1,2]}'), (2, 'alice ng', 'north', DATE '2026-01-05', 80.50, 'gadget', 'b', '{"channel":"shop","items":[3]}'), (3, 'Bob Okafor', 'south', DATE '2026-01-05', 300.00, 'widget', 'a|c', '{"channel":"web","items":[4,5,6]}'), (4, 'Carla Díaz', 'east', DATE '2026-01-09', NULL, 'gizmo', NULL, '{"channel":"web","items":[]}'), (5, 'Bob Okafor', 'south', DATE '2026-01-12', 45.25, 'gadget', 'c', '{"channel":"phone","items":[7]}'), (6, 'Dev Patel', 'west', DATE '2026-01-12', 510.00, 'widget', 'a', '{"channel":"web","items":[8,9]}'), (7, 'Carla Diaz', 'east', DATE '2026-01-20', 99.99, 'gizmo', 'b|c', '{"channel":"shop","items":[10]}'), (8, 'Dev Patel', 'west', DATE '2026-02-02', 510.00, 'widget', 'a', '{"channel":"web","items":[8,9]}'), (9, 'Eve Larsen', NULL, DATE '2026-02-03', 15.00, 'gadget', NULL, '{"channel":"web","items":[11]}'), (10, 'Alice Ng', 'north', DATE '2026-02-10', 220.00, 'gizmo', 'a|b|c', '{"channel":"phone","items":[12,13]}') ) t(order_id, customer, region, order_date, amount, product, tags, meta); CREATE TABLE customers AS SELECT * FROM (VALUES ('Alice Ng', 'retail', DATE '2025-06-01', 52.52, 13.40), ('Bob Okafor', 'wholesale', DATE '2025-09-15', 48.85, 2.35), ('Carla Diaz', 'retail', DATE '2025-11-30', 40.42, -3.70), ('Frank Moore', 'retail', DATE '2026-01-01', 51.51, -0.13) ) t(customer, segment, joined, lat, lon); ``` Then run the statement: ```sql CREATE TEMP TABLE summary AS SELECT count(*) AS orders, count(amount) AS priced, sum(amount) AS total, round(avg(amount), 2) AS mean, min(amount) AS smallest, max(amount) AS largest, count(DISTINCT customer) AS customers, min(order_date) AS first_day, max(order_date) AS last_day FROM orders; -- an empty input still gives one row CREATE TEMP TABLE from_nothing AS SELECT count(*) AS orders, sum(amount) AS total FROM orders WHERE false; SELECT * FROM summary; SELECT CASE WHEN ((SELECT count(*) FROM summary) = 1 AND (SELECT (orders, priced, total, mean, smallest, largest, customers, first_day, last_day) FROM summary) = (10, 9, 1900.74, 211.19, 15.00, 510.00, 8, DATE '2026-01-03', DATE '2026-02-10') AND (SELECT count(*) FROM from_nothing) = 1 AND (SELECT orders FROM from_nothing) = 0 AND (SELECT total FROM from_nothing) IS NULL) THEN 'as expected' ELSE error('the table did not reduce to one row of 10 orders, 9 priced, totalling 1900.74, or an empty table did not give one row of zero') END AS verdict; ``` It needs nothing installed beyond DuckDB itself. ## What other tools call it Meridian's classification; an independent blind rating agreed on 95.7% of 324 rows (variant, version 3). - Alteryx Designer: [`Count Records`](https://help.alteryx.com/current/en/designer/tools/transform/count-records-tool.html) (read 2026-09-27) - Apache DataFusion: [`count`](https://docs.rs/datafusion/latest/datafusion/dataframe/struct.DataFrame.html) (read 2026-09-27) - Duckle: [`Count Rows`](https://github.com/slothflowlabs/duckle/blob/f85f8960edfb6efbf4cb89d58a7b32e16df0e867/website/docs/components.html) (read 2026-09-25) - Duckle: [`Diff Summary`](https://github.com/slothflowlabs/duckle/blob/f85f8960edfb6efbf4cb89d58a7b32e16df0e867/frontend/src/workflow-ui/palette-data.ts) (read 2026-09-25) - Polars: [`DataFrame.count`](https://docs.pola.rs/api/python/stable/reference/dataframe/aggregation.html) (read 2026-09-27) - Polars: [`DataFrame.max`](https://docs.pola.rs/api/python/stable/reference/dataframe/aggregation.html) (read 2026-09-27) - Polars: [`DataFrame.mean`](https://docs.pola.rs/api/python/stable/reference/dataframe/aggregation.html) (read 2026-09-27) - Polars: [`DataFrame.median`](https://docs.pola.rs/api/python/stable/reference/dataframe/aggregation.html) (read 2026-09-27) - Polars: [`DataFrame.min`](https://docs.pola.rs/api/python/stable/reference/dataframe/aggregation.html) (read 2026-09-27) - Polars: [`DataFrame.product`](https://docs.pola.rs/api/python/stable/reference/dataframe/aggregation.html) (read 2026-09-27) - Polars: [`DataFrame.quantile`](https://docs.pola.rs/api/python/stable/reference/dataframe/aggregation.html) (read 2026-09-27) - Polars: [`DataFrame.std`](https://docs.pola.rs/api/python/stable/reference/dataframe/aggregation.html) (read 2026-09-27) - Polars: [`DataFrame.sum`](https://docs.pola.rs/api/python/stable/reference/dataframe/aggregation.html) (read 2026-09-27) - Polars: [`DataFrame.var`](https://docs.pola.rs/api/python/stable/reference/dataframe/aggregation.html) (read 2026-09-27) - RapidMiner Studio: [`Extract Aggregates`](https://docs.rapidminer.com/latest/studio/operators/modeling/time_series/feature_extraction/extract_std_descriptive_features.html) (read 2026-09-27) - RapidMiner Studio: [`Extract Mode`](https://docs.rapidminer.com/latest/studio/operators/modeling/time_series/feature_extraction/extract_mode.html) (read 2026-09-27) - VisiData: [`freq-summary`](https://github.com/saulpw/visidata/blob/7609212ec2febafa9caba7183cbebfaf49627909/visidata/freqtbl.py#L206) (read 2026-09-23) - VisiData: [`memo-aggregate`](https://github.com/saulpw/visidata/blob/7609212ec2febafa9caba7183cbebfaf49627909/visidata/aggregators.py#L410) (read 2026-09-23)