Whether the schema check passed.
Comparison.schema_ok: bool
A comparison checks the two tables’ schemas before it compares their values. How a schema difference is treated depends on schema= in compare(). With schema="strict" (the default), schema_ok is the same as SchemaDiff.matches, and a False value means that every test unit fails, so n_passed is 0. With schema="common", schema differences are only reported, so schema_ok is always True.
This makes schema_ok the property to check when a comparison fails and it’s not clear whether the values or the columns are to blame. The details of the difference are in schema_diff.
Here, the target table has the same rows as the source, plus a new humidity column.
import pointblank as pb
import polars as pl
readings = pl.DataFrame({"station": ["S1", "S2", "S3"], "temp": [12.1, 14.0, 9.8]})
readings_new = readings.with_columns(humidity=pl.Series([0.61, 0.58, 0.72]))
comparison = pb.compare(readings, readings_new, keys="station")
comparison.schema_ok, comparison.status_counts, comparison.n_passed(False, {'match': 3, 'changed': 0, 'missing': 0, 'extra': 0, 'dup_key': 0}, 0)Every row has a status of "match", but none of them pass, because the schema check failed. The cause is in the schema differences:
If the new column is expected, schema="common" compares the shared columns and lets the rows pass:
relaxed = pb.compare(readings, readings_new, keys="station", schema="common")
relaxed.schema_ok, relaxed.n_passed(True, 3)The new column is still listed in relaxed.schema_diff, so the change remains visible in the report even though it no longer fails the comparison.