Validate.col_missing_coded()

Validate that all missing values in a column are coded (no uncoded nulls).

Usage

Source

Validate.col_missing_coded(
    columns,
    missing,
    pre=None,
    segments=None,
    thresholds=None,
    actions=None,
    brief=None,
    active=True
)

The col_missing_coded() validation method checks that every absent value in a column is expressed with an explicit missing-value code, rather than a raw null. Under the structured missingness model (see MissingSpec), every absence should carry a reason — encoded as a sentinel value such as -99 for "not_asked". A raw null represents uncoded (unknown) missingness, so this validation treats raw nulls as failing test units while declared sentinel values and real values pass.

This validation operates over the number of test units 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.

missing: MissingSpec

A MissingSpec describing the sentinel values (and their reasons) that encode missingness for this column. The spec documents which codes are considered valid expressions of missingness.

pre: Callable | None = None

An 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.

segments: SegmentSpec | None = None

An optional directive on segmentation, which serves to split a validation step into multiple (one step per segment).

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

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 | Callable = True
A boolean value or callable that determines 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.

Segmentation

The segments= argument allows for the segmentation of a validation step into multiple segments. This is useful for applying the same validation step to different subsets of the data. The segmentation can be done based on a single column or specific fields within a column. Providing a single column name results in a separate validation step for each unique value in that column; a tuple of (column, values) restricts segmentation to the listed values. The segmentation is performed after any pre= preprocessing.

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

Here, the age column codes its missingness with sentinel values, except for one row that has a raw null (an uncoded absence):

import pointblank as pb
import polars as pl

tbl = pl.DataFrame({"age": [34, -98, 41, None, 29, -99, 55, 38]})

age_missing = pb.MissingSpec(
    reasons={-99: "not_asked", -98: "refused", -97: "dont_know"},
)

validation = (
    pb.Validate(data=tbl)
    .col_missing_coded(columns="age", missing=age_missing)
    .interrogate()
)

validation
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C66 1
col_missing_coded
col_missing_coded()
 age — ✓ 8 7
0.88		 1
0.12		 — — —
Notes

Step 1 (missing_spec) Missing codes: -99→not_asked, -98→refused, -97→dont_know, null→unknown

The validation reports a single failing test unit: the row where age is a raw null, which represents missingness without a documented reason.