Validate.col_vals_not_in_set

Validate.col_vals_not_in_set(
    columns,
    set,
    pre=None,
    thresholds=None,
    active=True,
)

Validate whether column values are not in a set of values.

The col_vals_not_in_set() validation method checks whether column values in a table are not part of a specified set= of values. This validation will operate over the number of test units that is equal to the number of rows in the table (determined after any pre= mutation has been applied).

Parameters

columns : str | list[str] | Column | ColumnSelector | ColumnSelectorNarwhals: A single column or a list of columns to validate. Can also use col() with column selectors to specify one or more columns. If multiple columns are supplied or resolved, there will be a separate validation step generated for each column.
set : list[float | int]: A list of values to compare against.
pre : Callable | None = None: A pre-processing function or lambda to apply to the data table for the validation step.
thresholds : int | float | bool | tuple | dict | Thresholds = None: Failure threshold levels so that the validation step can react accordingly when exceeding the set levels for different states (warn, stop, and notify). This can be created simply as an integer or float denoting the absolute number or fraction of failing test units for the ‘warn’ level. Otherwise, you can use a tuple of 1-3 values, a dictionary of 1-3 entries, or a Thresholds object.
active : bool = True: A boolean value indicating whether the validation step should be active. Using False

Returns

: Validate: The Validate object with the added validation step.

Examples

For the examples here, we’ll use a simple Polars DataFrame with two numeric columns (a and b). The table is shown below:

import pointblank as pb
import polars as pl

tbl = pl.DataFrame(
    {
        "a": [7, 8, 1, 9, 1, 7],
        "b": [1, 8, 2, 6, 9, 1],
    }
)

pb.preview(tbl)

	a Int64	b Int64
1	7	1
2	8	8
3	1	2
4	9	6
5	1	9
6	7	1

Let’s validate that none of the values in column a are in the set of [2, 3, 4, 5, 6]. We’ll determine if this validation had any failing test units (there are six test units, one for each row).

validation = (
    pb.Validate(data=tbl)
    .col_vals_not_in_set(columns="a", set=[2, 3, 4, 5, 6])
    .interrogate()
)

validation

		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	S	N	EXT
#4CA64C	1	col_vals_not_in_set()	a	2, 3, 4, 5, 6		✓	6	6 1.00	0 0.00	—	—	—	—

Printing the validation object shows the validation table in an HTML viewing environment. The validation table shows the single entry that corresponds to the validation step created by using col_vals_not_in_set(). All test units passed, and there are no failing test units.

Now, let’s use that same set of values for a validation on column b.

validation = (
    pb.Validate(data=tbl)
    .col_vals_not_in_set(columns="b", set=[2, 3, 4, 5, 6])
    .interrogate()
)

validation

		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	S	N	EXT
#4CA64C66	1	col_vals_not_in_set()	b	2, 3, 4, 5, 6		✓	6	4 0.67	2 0.33	—	—	—

The validation table reports two failing test units. The specific failing cases are for the column b values of 2 and 6, both of which are in the set of [2, 3, 4, 5, 6].