Validate.col_vals_regex

Validate.col_vals_regex(
    columns,
    pattern,
    na_pass=False,
    pre=None,
    thresholds=None,
    active=True,
)

Validate whether column values match a regular expression pattern.

The col_vals_regex() validation method checks whether column values in a table correspond to a pattern= matching 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

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.
pattern : str: A regular expression pattern to compare against.
na_pass : bool = False: Should any encountered None, NA, or Null values be considered as passing test units? By default, this is False. Set to True to pass test units with missing values.
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 two string columns (a and b). The table is shown below:

import pointblank as pb
import polars as pl

tbl = pl.DataFrame(
    {
        "a": ["rb-0343", "ra-0232", "ry-0954", "rc-1343"],
        "b": ["ra-0628", "ra-583", "rya-0826", "rb-0735"],
    }
)

pb.preview(tbl)

	a String	b String
1	rb-0343	ra-0628
2	ra-0232	ra-583
3	ry-0954	rya-0826
4	rc-1343	rb-0735

Let’s validate that all of the values in column a match a particular regex pattern. 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_regex(columns="a", pattern=r"r[a-z]-[0-9]{4}")
    .interrogate()
)

validation

		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	S	N	EXT
#4CA64C	1	col_vals_regex()	a	r[a-z]-[0-9]{4}		✓	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_regex(). All test units passed, and there are no failing test units.

Now, let’s use the same regex for a validation on column b.

validation = (
    pb.Validate(data=tbl)
    .col_vals_regex(columns="b", pattern=r"r[a-z]-[0-9]{4}")
    .interrogate()
)

validation

		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	S	N	EXT
#4CA64C66	1	col_vals_regex()	b	r[a-z]-[0-9]{4}		✓	4	2 0.50	2 0.50	—	—	—

The validation table reports two failing test units. The specific failing cases are for the string values of rows 1 and 2 in column b.