Validate.col_vals_expr

Validate.col_vals_expr(expr, pre=None, thresholds=None, active=True)

Validate column values using a custom expression.

The col_vals_expr() validation method checks whether column values in a table satisfy a custom expr= expression. 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

expr : any: A column expression that will evaluate each row in the table, returning a boolean value per table row. If the target table is a Polars DataFrame, the expression should either be a Polars column expression or a Narwhals one. For a Pandas DataFrame, the expression should either be a lambda expression or a Narwhals column expression.
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 will make the validation step inactive (still reporting its presence and keeping indexes for the steps unchanged).

Returns

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

Examples

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

import pointblank as pb
import polars as pl

tbl = pl.DataFrame(
    {
        "a": [1, 2, 1, 7, 8, 6],
        "b": [0, 0, 0, 1, 1, 1],
        "c": [0.5, 0.3, 0.8, 1.4, 1.9, 1.2],
    }
)

pb.preview(tbl)

	a Int64	b Int64	c Float64
1	1	0	0.5
2	2	0	0.3
3	1	0	0.8
4	7	1	1.4
5	8	1	1.9
6	6	1	1.2

Let’s validate that the values in column a are all integers. 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_expr(expr=pl.col("a") % 1 == 0)
    .interrogate()
)

validation

		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	S	N	EXT
#4CA64C	1	col_vals_expr()	—	COLUMN EXPR		✓	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_expr(). All test units passed, with no failing test units.