import pointblank as pb
game_revenue = pb.load_dataset("game_revenue")
pb.preview(game_revenue)PolarsRows2,000Columns11 |
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Validate.col_count_match
Validate.col_count_match(
count,
inverse=False,
pre=None,
thresholds=None,
actions=None,
brief=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 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
Thresholdsclass 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 meets or exceeds any set threshold levels. If provided, the
Actionsclass 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
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 | E | C | 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.