Validate.col_vals_regex

Validate.col_vals_regex(
    columns,
    pattern,
    na_pass=False,
    pre=None,
    thresholds=None,
    actions=None,
    brief=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 optional preprocessing function or lambda to apply to the data table during interrogation. This function should take a table as input and return a modified table. Have a look at the Preprocessing section for more information on how to use this argument.

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

Set threshold failure levels for reporting and reacting to exceedences of the levels. The thresholds are set at the step level and will override any global thresholds set in Validate(thresholds=...). The default is None, which means that no thresholds will be set locally and global thresholds (if any) will take effect. Look at the Thresholds section for information on how to set threshold levels.

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 | bool | None = None

An optional brief description of the validation step that will be displayed in the reporting table. You can use the templating elements like "{step}" to insert the step number, or "{auto}" to include an automatically generated brief. If True the entire brief will be automatically generated. If None (the default) then there won’t be a brief.

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.

Preprocessing

The pre= argument allows for a preprocessing function or lambda to be applied to the data table during interrogation. This function should take a table as input and return a modified table. This is useful for performing any necessary transformations or filtering on the data before the validation step is applied.

The preprocessing function can be any callable that takes a table as input and returns a modified table. For example, you could use a lambda function to filter the table based on certain criteria or to apply a transformation to the data. Note that you can refer to a column via columns= that is expected to be present in the transformed table, but may not exist in the table before preprocessing. Regarding the lifetime of the transformed table, it only exists during the validation step and is not stored in the Validate object or used in subsequent validation steps.

Thresholds

The thresholds= parameter is used to set the failure-condition levels for the validation step. If they are set here at the step level, these thresholds will override any thresholds set at the global level in Validate(thresholds=...).

There are three threshold levels: ‘warning’, ‘error’, and ‘critical’. The threshold values can either be set as a proportion failing of all test units (a value between 0 to 1), or, the absolute number of failing test units (as integer that’s 1 or greater).

Thresholds can be defined using one of these input schemes:

  1. use the Thresholds class (the most direct way to create thresholds)
  2. provide a tuple of 1-3 values, where position 0 is the ‘warning’ level, position 1 is the ‘error’ level, and position 2 is the ‘critical’ level
  3. create a dictionary of 1-3 value entries; the valid keys: are ‘warning’, ‘error’, and ‘critical’
  4. a single integer/float value denoting absolute number or fraction of failing test units for the ‘warning’ level only

If the number of failing test units exceeds set thresholds, the validation step will be marked as ‘warning’, ‘error’, or ‘critical’. All of the threshold levels don’t need to be set, you’re free to set any combination of them.

Aside from reporting failure conditions, thresholds can be used to determine the actions to take for each level of failure (using the actions= parameter).

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 E C EXT
#4CA64C 1
col_vals_regex
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 E C EXT
#4CA64C66 1
col_vals_regex
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.