expr_col()

Create a column expression for use in conjointly() validation.

Usage

Source

expr_col(column_name)

This function returns a ColumnExpression object that supports operations like >, <, +, etc. for use in 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() function to create a column expression for each of these conditions.

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
conjointly()
 COLUMN EXPR ✓ 6 1
0.17		 5
0.83		 — — —

The above code creates a validation object that checks the specified conditions using the expr_col() function. The resulting validation table will show whether each condition was satisfied for each row in the table.

See Also

The, function