Validate.col_exists

Validate.col_exists(
    columns,
    thresholds=None,
    actions=None,
    brief=None,
    active=True,
)

Validate whether one or more columns exist in the table.

The col_exists() method checks whether one or more columns exist in the target table. The only requirement is specification of the column names. Each validation step or expectation will operate over a single test unit, which is whether the column exists or not.

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.
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 a string columns (a) and a numeric column (b). The table is shown below:

import pointblank as pb
import polars as pl

tbl = pl.DataFrame(
    {
        "a": ["apple", "banana", "cherry", "date"],
        "b": [1, 6, 3, 5],
    }
)

pb.preview(tbl)

	a String	b Int64
1	apple	1
2	banana	6
3	cherry	3
4	date	5

Let’s validate that the columns a and b actually exist in the table. We’ll determine if this validation had any failing test units (each validation will have a single test unit).

validation = (
    pb.Validate(data=tbl)
    .col_exists(columns=["a", "b"])
    .interrogate()
)

validation

		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	E	C	EXT
#4CA64C	1	col_exists()	a	—		✓	1	1 1.00	0 0.00	—	—	—	—
#4CA64C	2	col_exists()	b	—		✓	1	1 1.00	0 0.00	—	—	—	—

Printing the validation object shows the validation table in an HTML viewing environment. The validation table shows two entries (one check per column) generated by the col_exists() validation step. Both steps passed since both columns provided in columns= are present in the table.

Now, let’s check for the existence of a different set of columns.

validation = (
    pb.Validate(data=tbl)
    .col_exists(columns=["b", "c"])
    .interrogate()
)

validation

		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	E	C	EXT
#4CA64C	1	col_exists()	b	—		✓	1	1 1.00	0 0.00	—	—	—	—
#4CA64C66	2	col_exists()	c	—		✓	1	0 0.00	1 1.00	—	—	—	—

The validation table reports one passing validation step (the check for column b) and one failing validation step (the check for column c, which doesn’t exist).