conjointly()
0.17
0.83
conjointly() validation expressions.
## Parameters
`column_name: str`
The name of the column to reference.
## Returns
`ColumnExpression`
A column expression that can be used in comparisons and operations.
## Examples
Let's say we have a table with three columns: `a`, `b`, and `c`. We want to validate that:
- The values in column `a` are greater than `2`.
- The values in column `b` are less than `7`.
- The sum of columns `a` and `b` is less than the values in column `c`.
We can use the [expr_col()](expr_col.md#pointblank.expr_col) function to create a column expression for each of these conditions.
``` python
import pointblank as pb
import polars as pl
tbl = pl.DataFrame(
{
"a": [5, 7, 1, 3, 9, 4],
"b": [6, 3, 0, 5, 8, 2],
"c": [10, 4, 8, 9, 10, 5],
}
)
# Using expr_col() to create backend-agnostic validation expressions
validation = (
pb.Validate(data=tbl)
.conjointly(
lambda df: pb.expr_col("a") > 2,
lambda df: pb.expr_col("b") < 7,
lambda df: pb.expr_col("a") + pb.expr_col("b") < pb.expr_col("c")
)
.interrogate()
)
validation
```
| STEP | COLUMNS | VALUES | TBL | EVAL | UNITS | PASS | FAIL | W | E | C | EXT | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| #4CA64C66 | 1 |
conjointly()
|
|
✓ | 6 | 1 0.17 |
5 0.83 |
-- | -- | -- | CSV |