get_tabular_report() (or set it globally with `pb.config(report_incl_dimensions=True)`). Consider a validation of some sales data that touches several dimensions:
``` python
import pointblank as pb
import polars as pl
sales = pl.DataFrame({
"order_id": [1, 2, 3, 4, 5, 6, 7, 8, 8, 10], # one duplicate (8)
"amount": [120.0, -5.0, 47.5, 0.0, 30.0, 155.0, 175.0, 95.0, 205.0, None],
"email": ["a@ex.com", "invalid", "c@ex.com", "d@ex.com", "nope",
"f@ex.com", "g@ex.com", "h@ex.com", "i@ex.com", "j@ex.com"],
"status": ["paid", "paid", "refund", "paid", "pending",
"paid", "paid", "refund", "paid", "paid"],
})
validation = (
pb.Validate(data=sales, tbl_name="sales", label="Sales data quality")
.col_vals_not_null(columns="amount") # Completeness
.col_vals_gt(columns="amount", value=0) # Validity
.col_vals_regex(columns="email", pattern=r"^[^@]+@[^@]+\.[^@]+$") # Validity
.col_vals_in_set(columns="status", set=["paid", "refund", "pending"]) # Validity
.rows_distinct(columns_subset=["order_id"]) # Uniqueness
.row_count_match(count=10) # Volume
.interrogate()
)
validation.get_tabular_report(incl_dimensions=True)
```
| Pointblank Validation | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Sales data quality
Polarssales
|
|||||||||||||
| STEP | COLUMNS | VALUES | TBL | EVAL | UNITS | PASS | FAIL | W | E | C | EXT | ||
| #4CA64C66 |
CM1
|
col_vals_not_null()
|
 |
✓ | 10 | 9 0.90 |
1 0.10 |
-- | -- | -- | CSV | ||
| #4CA64C66 |
VA2
|
col_vals_gt()
|
|
✓ | 10 | 7 0.70 |
3 0.30 |
-- | -- | -- | CSV | ||
| #4CA64C66 |
VA3
|
col_vals_regex()
|
|
✓ | 10 | 8 0.80 |
2 0.20 |
-- | -- | -- | CSV | ||
| #4CA64C |
VA4
|
col_vals_in_set()
|
|
✓ | 10 | 10 1.00 |
0 0.00 |
-- | -- | -- | -- | ||
| #4CA64C66 |
UQ5
|
rows_distinct()
|
|
✓ | 10 | 8 0.80 |
2 0.20 |
-- | -- | -- | CSV | ||
| #4CA64C |
VO6
|
row_count_match()
|
|
✓ | 1 | 1 1.00 |
0 0.00 |
-- | -- | -- | -- | ||
| Health Score84% Dimension ScoresCompleteness90%Validity83.33%Uniqueness80%Volume100% | |||||||||||||
| 2026-07-13 17:59:28 UTC< 1 s2026-07-13 17:59:28 UTC | |||||||||||||
get_scorecard(). It shows the overall health score prominently along with a per-dimension breakdown (a color-coded bar, the score, and passing/total test units):
``` python
validation.get_scorecard()
```
Dimension Scores -- sales |
||
|---|---|---|
Health Score: 84%
|
||
| DIMENSION | SCORE | UNITS |
| Completeness |
90%
|
9 / 10 |
| Validity |
83.33%
|
25 / 30 |
| Uniqueness |
80%
|
8 / 10 |
| Volume |
100%
|
1 / 1 |
assert_dimension_scores() with per-dimension minimums; it raises an `AssertionError` if any dimension is below its minimum (here, uniqueness is `80%`):
``` python
try:
validation.assert_dimension_scores(thresholds={"uniqueness": 95})
except AssertionError as e:
print(e)
```
Dimension health score(s) below the required minimum: uniqueness (80% < 95%)
Dimensions present in the thresholds but absent from the validation are ignored, and a call where every dimension meets its minimum simply returns without raising. You can also set the minimums globally with `config(dimension_thresholds=...)`, and then call the method without arguments:
``` python
pb.config(dimension_thresholds={"completeness": 95})
try:
validation.assert_dimension_scores()
except AssertionError as e:
print(e)
pb.config() # reset back to defaults
```
Dimension health score(s) below the required minimum: completeness (90% < 95%)
PointblankConfig(report_incl_header=True, report_incl_footer=True, report_incl_footer_timings=True, report_incl_footer_notes=True, report_incl_dimensions=False, preview_incl_header=True, dimension_map=None, dimension_weights=None, dimension_thresholds=None)
# Accessing Scores Programmatically
The scores are also included in the summary available to FinalActions via get_validation_summary(), under the `dimension_scores` and `overall_health_score` keys. This makes it easy to log or trend the health score over time:
``` python
def log_health():
summary = pb.get_validation_summary()
print(f"Overall health score: {summary['overall_health_score']}")
print(f"By dimension: {summary['dimension_scores']}")
(
pb.Validate(data=sales, final_actions=pb.FinalActions(log_health))
.col_vals_not_null(columns="amount")
.col_vals_gt(columns="amount", value=0)
.interrogate()
)
```
Overall health score: 80.0
By dimension: {'completeness': 90.0, 'validity': 70.0}
| Pointblank Validation | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2026-07-13|17:59:28
Polars
|
|||||||||||||
| STEP | COLUMNS | VALUES | TBL | EVAL | UNITS | PASS | FAIL | W | E | C | EXT | ||
| #4CA64C66 | 1 |
col_vals_not_null()
|
|
✓ | 10 | 9 0.90 |
1 0.10 |
-- | -- | -- | CSV | ||
| #4CA64C66 | 2 |
col_vals_gt()
|
|
✓ | 10 | 7 0.70 |
3 0.30 |
-- | -- | -- | CSV | ||
| 2026-07-13 17:59:28 UTC< 1 s2026-07-13 17:59:28 UTC | |||||||||||||