Get a data quality scorecard as a GT table.
Usage
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()).
title: str | None = ":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.
GTGT object representing the scorecard.
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.
Get the per-dimension scores behind the scorecard.
Get the overall score behind the scorecard.
Get the full per-step validation report.