Validate.col_vals_expr

Validate.col_vals_expr(
    expr,
    pre=None,
    thresholds=None,
    actions=None,
    brief=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 optional preprocessing function or lambda to apply to the data table during interrogation.

thresholds : int | float | bool | tuple | dict | Thresholds = None

Optional failure threshold levels for the validation step(s), so that the interrogation can react accordingly when exceeding the set levels for different states (‘warning’, ‘error’, and ‘critical’). This can be created using the Thresholds class or more simply as (1) an integer or float denoting the absolute number or fraction of failing test units for the ‘warn’ level, (2) a tuple of 1-3 values, or (3) a dictionary of 1-3 entries.

actions : Actions | None = None

Optional actions to take when the validation step meets or exceeds any set threshold levels. If provided, the Actions class should be used to define the actions.

brief : str | None = None

An optional brief description of the validation step. The templating elements "{col}" and "{step}" can be used to insert the column name and step number, respectively.

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	E	C	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.