Validate.col_vals_between

Validate.col_vals_between(
    columns,
    left,
    right,
    inclusive=(True, True),
    na_pass=False,
    pre=None,
    thresholds=None,
    actions=None,
    brief=None,
    active=True,
)

Do column data lie between two specified values or data in other columns?

The col_vals_between() validation method checks whether column values in a table fall within a range. The range is specified with three arguments: left=, right=, and inclusive=. The left= and right= values specify the lower and upper bounds. These bounds can be specified as literal values or as column names provided within col(). The 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.
left : float | int | Column: The lower bound of the range. This can be a single numeric value or a single column name given in col(). The latter option allows for a column-column comparison for the lower bound.
right : float | int | Column: The upper bound of the range. This can be a single numeric value or a single column name given in col(). The latter option allows for a column-column comparison for the upper bound.
inclusive : tuple[bool, bool] = (True, True): A tuple of two boolean values indicating whether the comparison should be inclusive. The position of the boolean values correspond to the left= and right= values, respectively. By default, both values are True.
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.
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 three numeric columns (a, b, and c). The table is shown below:

import pointblank as pb
import polars as pl

tbl = pl.DataFrame(
    {
        "a": [2, 3, 2, 4, 3, 4],
        "b": [5, 6, 1, 6, 8, 5],
        "c": [9, 8, 8, 7, 7, 8],
    }
)

pb.preview(tbl)

	a Int64	b Int64	c Int64
1	2	5	9
2	3	6	8
3	2	1	8
4	4	6	7
5	3	8	7
6	4	5	8

Let’s validate that values in column a are all between the fixed boundary values of 1 and 5. We’ll determine if this validation had any failing test units (there are six test units, one for each row).

validation = (
    pb.Validate(data=tbl)
    .col_vals_between(columns="a", left=1, right=5)
    .interrogate()
)

validation

		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	E	C	EXT
#4CA64C	1	col_vals_between()	a	[1, 5]		✓	6	6 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_between(). All test units passed, and there are no failing test units.

Aside from checking a column against two literal values representing the lower and upper bounds, we can also provide column names to the left= and/or right= arguments (by using the helper function col()). In this way, we can perform three additional comparison types:

left=column, right=column
left=literal, right=column
left=column, right=literal

For the next example, we’ll use col_vals_between() to check whether the values in column b are between than corresponding values in columns a (lower bound) and c (upper bound).

validation = (
    pb.Validate(data=tbl)
    .col_vals_between(columns="b", left=pb.col("a"), right=pb.col("c"))
    .interrogate()
)

validation

		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	E	C	EXT
#4CA64C66	1	col_vals_between()	b	[a, c]		✓	6	4 0.67	2 0.33	—	—	—

The validation table reports two failing test units. The specific failing cases are:

Row 2: b is 1 but the bounds are 2 (a) and 8 (c).
Row 4: b is 8 but the bounds are 3 (a) and 7 (c).