Validate.col_vals_null

Validate.col_vals_null(
    columns,
    pre=None,
    thresholds=None,
    actions=None,
    brief=None,
    active=True,
)

Validate whether values in a column are NULL.

The col_vals_null() validation method checks whether column values in a table are NULL. This validation will operate over the number of test units that is equal to the number of rows in the table.

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.
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(s) 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 two numeric columns (a and b). The table is shown below:

import pointblank as pb
import polars as pl

tbl = pl.DataFrame(
    {
        "a": [None, None, None, None],
        "b": [None, 2, None, 9],
    }
).with_columns(pl.col("a").cast(pl.Int64))

pb.preview(tbl)

	a Int64	b Int64
1	None	None
2	None	2
3	None	None
4	None	9

Let’s validate that values in column a are all Null values. We’ll determine if this validation had any failing test units (there are four test units, one for each row).

validation = (
    pb.Validate(data=tbl)
    .col_vals_null(columns="a")
    .interrogate()
)

validation

		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	E	C	EXT
#4CA64C	1	col_vals_null()	a	—		✓	4	4 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_null(). 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_null(columns="b")
    .interrogate()
)

validation

		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	E	C	EXT
#4CA64C66	1	col_vals_null()	b	—		✓	4	2 0.50	2 0.50	—	—	—

The validation table reports two failing test units. The specific failing cases are for the two non-Null values in column b.