Custom Expression for Checking Column Values

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

Pointblank Validation
2025-01-20|18:18:19
Pandas
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W S N EXT
#4CA64C 1
col_vals_expr
col_vals_expr() 		— COLUMN EXPR ✓ 13 13
1.00		 0
0.00		 — — — —
2025-01-20 18:18:19 UTC< 1 s2025-01-20 18:18:19 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
PandasRows13Columns8
date_time
Datetime
date
Datetime
a
Int64
b
String
c
Float64
d
Float64
e
Boolean
f
String
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