Validate.get_dataframe_report()

Get a report of the validation results as a DataFrame.

Usage

Source

Validate.get_dataframe_report(tbl_type="polars")

The get_dataframe_report() method returns a compact, row-wise summary of validation step results as a DataFrame. Each row corresponds to a single validation step. This is useful for logging, exporting (e.g., writing to CSV or Parquet), and programmatic analysis of validation outcomes.

The output format depends on tbl_type=: Polars DataFrame (default), Pandas DataFrame, or DuckDB via an Ibis memtable.

Parameters

tbl_type: Literal["polars", "pandas", "duckdb"] = "polars"

The output backend. One of "polars" (default), "pandas", or "duckdb".

Returns

polars.DataFrame | pandas.DataFrame | ibis.expr.types.relations.Table

A tabular summary of validation results. When tbl_type="duckdb", the return value is an Ibis memtable (a Table expression).

Raises

ValueError

If tbl_type= is not one of "polars", "pandas", or "duckdb".

ImportError

If the required library for the chosen tbl_type= is not installed.

Output Columns

The returned DataFrame contains the following columns:

• active: Whether the validation step was active (True/False).
• step_number: The 1-indexed step number.
• step_description: The assertion type (e.g., "col_vals_gt").
• columns: The column name validated.
• values: The comparison value used in the validation. For regex validations, just the pattern string is included.
• step_evaluated: Whether the step was evaluated without error.
• units: Total number of test units.
• all_units_passed: Whether every test unit passed.
• pass_n: Number of passing test units.
• pass_pct: Fraction of test units that passed (0.0-1.0).
• failed_n: Number of failing test units.
• failed_pct: Fraction of test units that failed (0.0-1.0).
• warning, error, critical: Whether the respective threshold was exceeded.
• brief: A coalesced description of the step (from manual brief or auto-generated brief).
• preprocessed: Whether a preprocessing function was applied.
• segmented: Whether the step used segmented validation.

For inactive steps (active=False), the result columns (step_evaluated through critical) are set to None/null.

Examples

Create a validation, interrogate, and get the results as a Polars DataFrame:

import pointblank as pb

validation = (
    pb.Validate(
        data=pb.load_dataset("small_table", tbl_type="polars"),
        label="My validation",
    )
    .col_vals_gt(columns="d", value=100)
    .col_vals_regex(columns="b", pattern=r"[0-9]-[a-z]{3}-[0-9]{3}")
    .interrogate()
)

validation.get_dataframe_report()

shape: (2, 18)

active	step_number	step_description	columns	values	step_evaluated	units	all_units_passed	pass_n	pass_pct	failed_n	failed_pct	warning	error	critical	brief	preprocessed	segmented
bool	i64	str	str	object	bool	i64	bool	i64	f64	i64	f64	bool	bool	bool	str	bool	bool
true	1	"col_vals_gt"	"d"	100	null	13	true	13	1.0	0	0.0	null	null	null	"Expect that values in `d` shou…	false	false
true	2	"col_vals_regex"	"b"	[0-9]-[a-z]{3}-[0-9]{3}	null	13	true	13	1.0	0	0.0	null	null	null	"Expect that values in `b` shou…	false	false

Get the results as a Pandas DataFrame instead:

validation.get_dataframe_report(tbl_type="pandas")

	active	step_number	step_description	columns	values	step_evaluated	units	all_units_passed	pass_n	pass_pct	failed_n	failed_pct	warning	error	critical	brief	preprocessed	segmented
0	True	1	col_vals_gt	d	100	None	13	True	13	1.0	0	0.0	None	None	None	Expect that values in `d` should be > `100`.	False	False
1	True	2	col_vals_regex	b	[0-9]-[a-z]{3}-[0-9]{3}	None	13	True	13	1.0	0	0.0	None	None	None	Expect that values in `b` should match the reg...	False	False