Feat active expression

[rich-iannone]

This PR enhances the active= parameter on validation methods, the parameter can now accept a callable in addition to a boolean. When a callable is provided, it is evaluated against the target table before the step runs. If it returns False (or raises an exception), the step is skipped and a descriptive, locale-aware note is attached to the validation report explaining why.

Two new helper functions (has_columns() and has_rows()) provide common pre-check patterns out of the box. Here's an example where several steps are skipped due to various active= parameter inputs:

import polars as pl
import pointblank as pb

my_table = pl.DataFrame({
    "a": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    "c": [5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
    "revenue": [100, 250, 50, 300, 175, 400, 90, 125, 210, 330],
    "score": [45, 72, 88, 33, 61, 95, 50, 77, 42, 68],
})

validation = (
    pb.Validate(data=my_table)
    .col_vals_gt(
        columns="revenue",
        value=0,
        active=False
    )
    .col_vals_lt(
        columns="score",
        value=100,
        active=pb.has_rows(min=100),  # skip if fewer than 10 rows
    )
    .col_vals_gt(
        columns="c",
        value=pb.col("a"),
        active=lambda tbl: "b" in tbl.columns,  # custom callable
    )
    .interrogate()
)

validation

Fixes: #347

[phobson]

thanks!