import pointblank as pb
game_revenue = pb.load_dataset("game_revenue")
pb.preview(game_revenue)PolarsRows2000Columns11 |
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Validate.col_count_match
Validate.col_count_match(
count,
inverse=False,
pre=None,
thresholds=None,
active=True,
)Validate whether the column count of the table matches a specified count.
The col_count_match() method checks whether the column count of the target table matches a specified count. This validation will operate over a single test unit, which is whether the column count matches the specified count.
We also have the option to invert the validation step by setting inverse=True. This will make the expectation that column row count of the target table does not match the specified count.
Parameters
count : int | FrameT | Any-
The expected column count of the table. This can be an integer value, a Polars or Pandas DataFrame object, or an Ibis backend table. If a DataFrame/table is provided, the column count of that object will be used as the expected count.
inverse : bool = False-
Should the validation step be inverted? If
True, then the expectation is that the column count of the target table should not match the specifiedcount=value. pre : Callable | None = None-
A pre-processing function or lambda to apply to the data table for the validation step.
thresholds : int | float | bool | tuple | dict | Thresholds = None-
Failure threshold levels so that the validation step can react accordingly when exceeding the set levels for different states (
warn,stop, andnotify). This can be created simply as an integer or float denoting the absolute number or fraction of failing test units for the ‘warn’ level. Otherwise, you can use a tuple of 1-3 values, a dictionary of 1-3 entries, or a Thresholds object. active : bool = True-
A boolean value indicating whether the validation step should be active. Using
Falsewill make the validation step inactive (still reporting its presence and keeping indexes for the steps unchanged).
Returns
: Validate-
The
Validateobject with the added validation step.
Examples
For the examples here, we’ll use the built in dataset "game_revenue". The table can be obtained by calling load_dataset("game_revenue").
Let’s validate that the number of columns in the table matches a fixed value. In this case, we will use the value 11 as the expected column count.
validation = (
pb.Validate(data=game_revenue)
.col_count_match(count=11)
.interrogate()
)
validation| STEP | COLUMNS | VALUES | TBL | EVAL | UNITS | PASS | FAIL | W | S | N | EXT | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| #4CA64C | 1 |
|
✓ | 1 | 1 1.00 |
0 0.00 |
— | — | — | — |
The validation table shows that the expectation value of 11 matches the actual count of columns in the target table. So, the single test unit passed.