Mutate the Table in a Validation Step

For far more specialized validations, modify the table with the pre= argument before checking it.

Pointblank Validation
2025-01-20|18:18:04 Polars
		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	S	N	EXT
#4CA64C	1	col_vals_between()	a	[3, 6]		✓	1	1 1.00	0 0.00	—	—	—	—
#4CA64C	2	col_vals_eq()	b_len	9		✓	13	13 1.00	0 0.00	—	—	—	—
2025-01-20 18:18:04 UTC< 1 s2025-01-20 18:18:04 UTC

import pointblank as pb
import polars as pl
import narwhals as nw

validation = (
    pb.Validate(
        data=pb.load_dataset(dataset="small_table", tbl_type="polars")
    )
    .col_vals_between(
        columns="a",
        left=3, right=6,
        pre=lambda df: df.select(pl.median("a"))    # Use a Polars expression to aggregate
    )
    .col_vals_eq(
        columns="b_len",
        value=9,
        pre=lambda dfn: dfn.with_columns(           # Use a Narwhals expression, identified
            b_len=nw.col("b").str.len_chars()       # by the 'dfn' here
        )
    )
    .interrogate()
)

validation

Preview of Input Table

PolarsRows13Columns8
	date_time Datetime	date Date	a Int64	b String	c Int64	d Float64	e Boolean	f String
1	2016-01-04 11:00:00	2016-01-04	2	1-bcd-345	3	3423.29	True	high
2	2016-01-04 00:32:00	2016-01-04	3	5-egh-163	8	9999.99	True	low
3	2016-01-05 13:32:00	2016-01-05	6	8-kdg-938	3	2343.23	True	high
4	2016-01-06 17:23:00	2016-01-06	2	5-jdo-903	None	3892.4	False	mid
5	2016-01-09 12:36:00	2016-01-09	8	3-ldm-038	7	283.94	True	low
6	2016-01-11 06:15:00	2016-01-11	4	2-dhe-923	4	3291.03	True	mid
7	2016-01-15 18:46:00	2016-01-15	7	1-knw-093	3	843.34	True	high
8	2016-01-17 11:27:00	2016-01-17	4	5-boe-639	2	1035.64	False	low
9	2016-01-20 04:30:00	2016-01-20	3	5-bce-642	9	837.93	False	high
10	2016-01-20 04:30:00	2016-01-20	3	5-bce-642	9	837.93	False	high
11	2016-01-26 20:07:00	2016-01-26	4	2-dmx-010	7	833.98	True	low
12	2016-01-28 02:51:00	2016-01-28	2	7-dmx-010	8	108.34	False	low
13	2016-01-30 11:23:00	2016-01-30	1	3-dka-303	None	2230.09	True	high