col_vals_decreasing()
1.00
0.00
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.
`allow_stationary: bool = ``False`
An option to allow pauses in decreasing values. For example, if the values for the test units are `[88, 85, 85, 82, 80]` then the third unit (`85`, appearing a second time) would be marked as failing when `allow_stationary` is `False`. Using `allow_stationary=True` will result in all the test units in `[88, 85, 85, 82, 80]` to be marked as passing.
`increasing_tol: float | None = None`
An optional threshold value that allows for movement of numerical values in the positive direction. By default this is `None` but using a numerical value will set the absolute threshold of positive travel allowed across numerical test units. Note that setting a value here also has the effect of setting `allow_stationary` to `True`.
`na_pass: bool = ``False`
Should any encountered None, NA, or Null values be considered as passing test units? By default, this is `False`. Set to `True` to pass test units with missing values.
`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.
`segments: SegmentSpec | None = None`
An optional directive on segmentation, which serves to split a validation step into multiple (one step per segment). Can be a single column name, a tuple that specifies a column name and its corresponding values to segment on, or a combination of both (provided as a list). Read the *Segmentation* section for usage information.
`thresholds: int | float | bool | tuple | dict | Thresholds | None = 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 is `None`, 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(s) meets or exceeds any set threshold levels. If provided, the Actions class 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. If `True` the entire brief will be automatically generated. If `None` (the default) then there won't be a brief.
`active: bool | Callable = ``True`
A boolean value or callable that determines 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). A callable can also be provided; it will receive the data table as its single argument and must return a boolean value. The callable is evaluated *before* any `pre=` processing. Inspection functions like has_columns() and has_rows() can be used here to conditionally activate a step based on properties of the target table.
`dimension: str | None = None`
An optional data quality dimension to categorize this validation step for health scoring. One of `"completeness"`, `"validity"`, `"uniqueness"`, `"consistency"`, `"timeliness"`, or `"volume"` (or any custom string). If `None` (the default), the dimension is inferred automatically from the assertion type. This label appears in the validation report and feeds the overall and per-dimension health scores.
## Returns
`Validate`
The [Validate](Validate.md#pointblank.Validate) object with the added validation step.
## Notes
This validation method is not supported for PySpark DataFrames. PySpark DataFrames are represented as LazyFrames in Narwhals, which do not support order-dependent operations like `shift()`. Calling this method on a PySpark DataFrame will raise an `InvalidOperationError` during interrogation.
## Examples
For the examples here, we'll use a simple Polars DataFrame with a numeric column (`a`). The table is shown below:
``` python
import pointblank as pb
import polars as pl
tbl = pl.DataFrame(
{
"a": [6, 5, 4, 3, 2, 1],
"b": [5, 4, 4, 3, 2, 1],
"c": [5, 4, 5, 3, 2, 1],
}
)
pb.preview(tbl)
```
a
Int64
|
b
Int64
|
c
Int64
|
|
|---|---|---|---|
| STEP | COLUMNS | VALUES | TBL | EVAL | UNITS | PASS | FAIL | W | E | C | EXT | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| #4CA64C | 1 |
col_vals_decreasing()
|
|
✓ | 6 | 6 1.00 |
0 0.00 |
-- | -- | -- | -- |
| STEP | COLUMNS | VALUES | TBL | EVAL | UNITS | PASS | FAIL | W | E | C | EXT | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| #4CA64C66 | 1 |
col_vals_decreasing()
|
|
✓ | 6 | 5 0.83 |
1 0.17 |
-- | -- | -- | CSV |
| STEP | COLUMNS | VALUES | TBL | EVAL | UNITS | PASS | FAIL | W | E | C | EXT | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| #4CA64C | 1 |
col_vals_decreasing()
|
|
✓ | 6 | 6 1.00 |
0 0.00 |
-- | -- | -- | -- |