Custom Expression for Checking Column Values

A column expression can be used to check column values. Just use col_vals_expr() for this.

Custom Expression for Checking Column Values

A column expression can be used to check column values. Just use col_vals_expr() for this.

		STEP	COLUMNS	VALUES	EVAL	UNITS	PASS	FAIL	W	E	C	EXT
Pointblank Validation
2026-04-13\|17:02:37 Pandas
#4CA64C	1	col_vals_expr()	—	COLUMN EXPR	✓	13	13 1.00	0 0.00	—	—	—	—
2026-04-13 17:02:37 UTC< 1 s2026-04-13 17:02:37 UTC

import pointblank as pb

validation = (
    pb.Validate(
        data=pb.load_dataset(dataset="small_table", tbl_type="pandas")
    )
    .col_vals_expr(expr=lambda df: (df["d"] % 1 != 0) & (df["a"] < 10))  # Pandas column expr
    .interrogate()
)

validation

Preview of Input Table

	date_time datetime64[us]	date datetime64[us]	a int64	b str	c float64	d float64	e bool	f str
PandasRows13Columns8
1	2016-01-04 11:00:00	2016-01-04 00:00:00	2	1-bcd-345	3.0	3423.29	True	high
2	2016-01-04 00:32:00	2016-01-04 00:00:00	3	5-egh-163	8.0	9999.99	True	low
3	2016-01-05 13:32:00	2016-01-05 00:00:00	6	8-kdg-938	3.0	2343.23	True	high
4	2016-01-06 17:23:00	2016-01-06 00:00:00	2	5-jdo-903	NA	3892.4	False	mid
5	2016-01-09 12:36:00	2016-01-09 00:00:00	8	3-ldm-038	7.0	283.94	True	low
6	2016-01-11 06:15:00	2016-01-11 00:00:00	4	2-dhe-923	4.0	3291.03	True	mid
7	2016-01-15 18:46:00	2016-01-15 00:00:00	7	1-knw-093	3.0	843.34	True	high
8	2016-01-17 11:27:00	2016-01-17 00:00:00	4	5-boe-639	2.0	1035.64	False	low
9	2016-01-20 04:30:00	2016-01-20 00:00:00	3	5-bce-642	9.0	837.93	False	high
10	2016-01-20 04:30:00	2016-01-20 00:00:00	3	5-bce-642	9.0	837.93	False	high
11	2016-01-26 20:07:00	2016-01-26 00:00:00	4	2-dmx-010	7.0	833.98	True	low
12	2016-01-28 02:51:00	2016-01-28 00:00:00	2	7-dmx-010	8.0	108.34	False	low
13	2016-01-30 11:23:00	2016-01-30 00:00:00	1	3-dka-303	NA	2230.09	True	high