# has_columns() Check whether one or more columns exist in a table. Usage ``` python has_columns(*columns) ``` This function returns a callable that, when given a table, checks whether all specified columns are present. It is primarily designed for use with the `active=` parameter of validation methods. When a validation step has `active=has_columns("col_a", "col_b")`, the step will be skipped (made inactive) if either `col_a` or `col_b` is missing from the target table. The callable is evaluated against the original table *before* any `pre=` processing is applied. This means the column check is performed on the raw input data, not on a pre-processed version of it. A note is attached to any skipped step in the validation report explaining which columns were not found. ## Parameters `*columns: str | list[str]` One or more column names to check for in the table. Each argument can be a string or a list of strings. All specified columns must be present for the callable to return `True`. ## Returns `Callable[[Any], bool]` A callable that accepts a table and returns `True` if every column in `columns` exists in the table, `False` otherwise. ## Raises `ValueError` If no column names are provided. `TypeError` If any of the provided column names is not a string or list of strings. ## Examples Using [has_columns()](has_columns.md#pointblank.has_columns) with the `active=` parameter to conditionally run a validation step: ``` python import pointblank as pb import polars as pl tbl = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) validation = ( pb.Validate(data=tbl) .col_vals_gt(columns="a", value=0, active=pb.has_columns("a")) .col_vals_gt(columns="a", value=0, active=pb.has_columns("z")) .interrogate() ) validation ```
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C 1
col_vals_gt()
a 0 ✓ 3 3
1.00		 0
0.00		 -- -- -- --
#4CA64C66 2
col_vals_gt()
a 0 -- -- -- -- -- -- -- --
Notes

Step 2 (active_check) Step skipped -- Column check failed: missing column(s) z.
The first step ran because column `a` exists. The second step was skipped because column `z` is missing, and the report note explains which column was not found. When checking for multiple columns, the step is only active when *all* columns are present: ``` python validation = ( pb.Validate(data=tbl) .col_vals_gt(columns="a", value=0, active=pb.has_columns("a", "b")) .col_vals_gt(columns="a", value=0, active=pb.has_columns("a", "x", "y")) .interrogate() ) validation ```
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C 1
col_vals_gt()
a 0 ✓ 3 3
1.00		 0
0.00		 -- -- -- --
#4CA64C66 2
col_vals_gt()
a 0 -- -- -- -- -- -- -- --
Notes

Step 2 (active_check) Step skipped -- Column check failed: missing column(s) x, y.
The first step is active because both `a` and `b` exist. The second step is skipped because `x` and `y` are missing. Column names can also be provided as a list: ``` python validation = ( pb.Validate(data=tbl) .col_vals_gt(columns="a", value=0, active=pb.has_columns(["a", "b"])) .interrogate() ) validation ```
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C 1
col_vals_gt()
a 0 ✓ 3 3
1.00		 0
0.00		 -- -- -- --