Provides a dictionary of the fraction of test units that failed for each validation step.
A measure of the fraction of test units that failed is provided by the f_failed attribute. This is the fraction of test units that failed the validation step over the total number of test units. Given this is a fractional value, it will always be in the range of 0 to 1.
Test units are the atomic units of the validation process. Different validations can have different numbers of test units. For example, a validation that checks for the presence of a column in a table will have a single test unit. A validation that checks for the presence of a value in a column will have as many test units as there are rows in the table.
This method provides a dictionary of the fraction of failing test units for each validation step. If the scalar=True argument is provided and i= is a scalar, the value is returned as a scalar instead of a dictionary. Furthermore, a value obtained here will be the complement to the analogous value returned by the f_passed() method (i.e., 1 - f_passed()).
i : int | list[int] | None = NoneThe validation step number(s) from which the fraction of failing test units is obtained. Can be provided as a list of integers or a single integer. If None, all steps are included.
scalar : bool = FalseIf True and i= is a scalar, return the value as a scalar instead of a dictionary.
: dict[int, float] | floatA dictionary of the fraction of failing test units for each validation step or a scalar value.
In the example below, we’ll use a simple Polars DataFrame with three columns (a, b, and c). There will be three validation steps, all having some failing test units. After interrogation, the f_failed() method is used to determine the fraction of failing test units for each validation step.
import polars as pl
import pointblank as pb
tbl = pl.DataFrame(
{
"a": [7, 4, 9, 7, 12, 3, 10],
"b": [9, 8, 10, 5, 10, 6, 2],
"c": ["a", "b", "c", "a", "b", "d", "c"]
}
)
validation = (
pb.Validate(data=tbl)
.col_vals_gt(columns="a", value=5)
.col_vals_gt(columns="b", value=pb.col("a"))
.col_vals_in_set(columns="c", set=["a", "b"])
.interrogate()
)
validation.f_failed(){1: 0.2857142857142857, 2: 0.42857142857142855, 3: 0.42857142857142855}The returned dictionary shows the fraction of failing test units for each validation step. The values are all greater than 0 since there were failing test units in each step.
If we wanted to check the fraction of failing test units for a single validation step, we can provide the step number. Also, we could have the value returned as a scalar by setting scalar=True (ensuring that i= is a scalar).
The returned value is the proportion of failing test units for the first validation step (2 failing test units out of 7 total test units).