Determine if every validation step passed perfectly, with no failing test units.
The all_passed() method determines if every validation step passed perfectly, with no failing test units. This method is useful for quickly checking if the table passed all validation steps with flying colors. If there’s even a single failing test unit in any validation step, this method will return False.
This validation metric might be overly stringent for some validation plans where failing test units are generally expected (and the strategy is to monitor data quality over time). However, the value of all_passed() could be suitable for validation plans designed to ensure that every test unit passes perfectly (e.g., checks for column presence, null-checking tests, etc.).
: boolTrue if all validation steps had no failing test units, False otherwise.
In the example below, we’ll use a simple Polars DataFrame with three columns (a, b, and c). There will be three validation steps, and the second step will have a failing test unit (the value 10 isn’t less than 9). After interrogation, the all_passed() method is used to determine if all validation steps passed perfectly.
import polars as pl
import pointblank as pb
tbl = pl.DataFrame(
{
"a": [1, 2, 9, 5],
"b": [5, 6, 10, 3],
"c": ["a", "b", "a", "a"],
}
)
validation = (
pb.Validate(data=tbl)
.col_vals_gt(columns="a", value=0)
.col_vals_lt(columns="b", value=9)
.col_vals_in_set(columns="c", set=["a", "b"])
.interrogate()
)
validation.all_passed()FalseThe returned value is False since the second validation step had a failing test unit. If it weren’t for that one failing test unit, the return value would have been True.