| Pointblank Validation | |||||||||||||
2025-01-20|18:17:49 DuckDB |
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| STEP | COLUMNS | VALUES | TBL | EVAL | UNITS | PASS | FAIL | W | S | N | EXT | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| #4CA64C | 1 |
|
✓ | 1 | 1 1.00 |
0 0.00 |
— | — | — | — | |||
| #4CA64C | 2 |
|
✓ | 1 | 1 1.00 |
0 0.00 |
— | — | — | — | |||
| #4CA64C | 3 |
|
✓ | 1 | 1 1.00 |
0 0.00 |
— | — | — | — | |||
| #4CA64C | 4 |
|
✓ | 1 | 1 1.00 |
0 0.00 |
— | — | — | — | |||
2025-01-20 18:17:49 UTC< 1 s2025-01-20 18:17:49 UTC |
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Verifying Row and Column Counts
Check the dimensions of the table with the *_count_match() validation methods.
import pointblank as pb
validation = (
pb.Validate(
data=pb.load_dataset(dataset="game_revenue", tbl_type="duckdb")
)
.col_count_match(count=11) # expect 11 columns in the table
.row_count_match(count=2000) # expect 2,000 rows in the table
.row_count_match(count=0, inverse=True) # expect that the table has rows
.col_count_match( # compare column count against
count=pb.load_dataset( # that of another table
dataset="game_revenue", tbl_type="pandas"
)
)
.interrogate()
)
validationPreview of Input Table
DuckDBRows2000Columns11 |
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