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-
An optional preprocessing function or lambda to apply to the data table during interrogation. This function should take a table as input and return a modified table. Have a look at the Preprocessing section for more information on how to use this argument.
thresholds :int|float|bool|tuple|dict| Thresholds = None-
Set threshold failure levels for reporting and reacting to exceedences of the levels. The thresholds are set at the step level and will override any global thresholds set in
Validate(thresholds=...). The default isNone, which means that no thresholds will be set locally and global thresholds (if any) will take effect. Look at the Thresholds section for information on how to set threshold levels. 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|bool| None = None-
An optional brief description of the validation step that will be displayed in the reporting table. You can use the templating elements like
"{step}"to insert the step number, or"{auto}"to include an automatically generated brief. IfTruethe entire brief will be automatically generated. IfNone(the default) then there won’t be a brief. 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.
Preprocessing
The pre= argument allows for a preprocessing function or lambda to be applied to the data table during interrogation. This function should take a table as input and return a modified table. This is useful for performing any necessary transformations or filtering on the data before the validation step is applied.
The preprocessing function can be any callable that takes a table as input and returns a modified table. For example, you could use a lambda function to filter the table based on certain criteria or to apply a transformation to the data. Regarding the lifetime of the transformed table, it only exists during the validation step and is not stored in the Validate object or used in subsequent validation steps.
Thresholds
The thresholds= parameter is used to set the failure-condition levels for the validation step. If they are set here at the step level, these thresholds will override any thresholds set at the global level in Validate(thresholds=...).
There are three threshold levels: ‘warning’, ‘error’, and ‘critical’. The threshold values can either be set as a proportion failing of all test units (a value between 0 to 1), or, the absolute number of failing test units (as integer that’s 1 or greater).
Thresholds can be defined using one of these input schemes:
- use the
Thresholdsclass (the most direct way to create thresholds) - provide a tuple of 1-3 values, where position
0is the ‘warning’ level, position1is the ‘error’ level, and position2is the ‘critical’ level - create a dictionary of 1-3 value entries; the valid keys: are ‘warning’, ‘error’, and ‘critical’
- a single integer/float value denoting absolute number or fraction of failing test units for the ‘warning’ level only
If the number of failing test units exceeds set thresholds, the validation step will be marked as ‘warning’, ‘error’, or ‘critical’. All of the threshold levels don’t need to be set, you’re free to set any combination of them.
Aside from reporting failure conditions, thresholds can be used to determine the actions to take for each level of failure (using the actions= parameter).
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 |
col_count_match()
|
✓ | 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.