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]

A single column or a list of columns to validate. If multiple columns are supplied, 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 polars as pl

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

tbl

shape: (4, 2)

a	b
str	str
"rb-0343"	"ra-0628"
"ra-0232"	"ra-583"
"ry-0954"	"rya-0826"
"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).

import pointblank as pb

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

validation

Pointblank Validation
2024-12-20|15:09:09 Polars
		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	S	N	EXT
#4CA64C	1	col_vals_regex()	a	r[a-z]-\d{4}		✓	4	4 1.00	0 0.00	—	—	—	—
2024-12-20 15:09:09 UTC< 1 s2024-12-20 15:09:09 UTC

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]-\d{4}")
    .interrogate()
)

validation

Pointblank Validation
2024-12-20|15:09:09 Polars
		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	S	N	EXT
#4CA64C66	1	col_vals_regex()	b	r[a-z]-\d{4}		✓	4	2 0.50	2 0.50	—	—	—
2024-12-20 15:09:09 UTC< 1 s2024-12-20 15:09:09 UTC

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.