Validate.get_scorecard()

Get a data quality scorecard as a GT table.

Usage

Source

Validate.get_scorecard(
    title=":default:",
    by=None,
)

The get_scorecard() method produces a compact, standalone scorecard that summarizes data quality across dimensions. It shows the overall health score prominently, along with a per-dimension breakdown (a color-coded bar, the dimension’s score, and its passing/total test units). Unlike the full validation report, the scorecard focuses purely on the aggregate health picture, making it well-suited for dashboards and executive summaries.

The returned object is a Great Tables GT object, so it can be displayed directly, exported to HTML (via .as_raw_html()), or saved to an image file (via .save()).

Parameters

title: str | None = ":default:"

Options for customizing the title of the scorecard. The default ":default:" produces a generic title (optionally including the table name). Use ":tbl_name:" to show just the table name, ":none:" for no title, or provide your own Markdown text.

by: str | None = None

The name of a column that steps are segmented by (with segments=), for a scorecard with a row for each segment: its health score as a bar, and a badge for each dimension with the dimension’s score. The scores are those of get_dimension_scores(by=).

Returns

GT

A GT object representing the scorecard.

Examples

Let’s validate the small_table dataset with steps in three dimensions and get the scorecard.

import pointblank as pb

small_table = pb.load_dataset(dataset="small_table", tbl_type="polars")

validation = (
    pb.Validate(data=small_table, tbl_name="small_table")
    .col_vals_not_null(columns="c")
    .col_vals_gt(columns="d", value=1000)
    .rows_distinct()
    .interrogate()
)

validation.get_scorecard()

Dimension Scores — small_table
Health Score: 74%
DIMENSION	SCORE	UNITS
Completeness	84.62%	11 / 13
Validity	53.85%	7 / 13
Uniqueness	84.62%	11 / 13

The overall health score (74%) is at the top, and each dimension has its own row with a bar, its score, and the passing and total test units. Validity has the shortest bar, since only 7 of the 13 values in d are above 1000. The scorecard leaves out the details of individual steps, which makes it a good summary to share, while get_tabular_report() gives the full picture for whoever has to fix the data.

With steps segmented by a column, by= gives a scorecard with a row for each segment.

validation = (
    pb.Validate(data=small_table, tbl_name="small_table")
    .col_vals_not_null(columns="c", segments="f")
    .col_vals_gt(columns="d", value=1000, segments="f")
    .interrogate()
)

validation.get_scorecard(by="f")

Dimension Scores — small_table
Health Score: 69%
f	HEALTH SCORE	DIMENSION	UNITS
high	67%	Completeness 83%Validity 50%	8 / 12
low	70%	Completeness 100%Validity 40%	7 / 10
mid	75%	Completeness 50%Validity 100%	3 / 4

Each segment has its health score and a badge with the score of each dimension. The rows with f of "mid" pass 3 of their 4 test units, while those with "low" pass 7 of 10, all of the failures being in validity.

See Also

get_dimension_scores()

Get the per-dimension scores behind the scorecard.

get_health_score()

Get the overall score behind the scorecard.

get_tabular_report()

Get the full per-step validation report.