Testing schemas

The team fixed the values in their expected DataFrame, but Polars inferred total_checkouts as 64-bit while the pipeline produces 32-bit. The two schemas are printed for you so you can see the mismatch. Fix the expected dtype, then assert the schemas match.

actual, expected, and assert_schema_equal are preloaded.

This exercise is part of the course

Scaling and Optimizing Data Pipelines with Polars

View Course

Exercise instructions

Cast total_checkouts in expected to pl.Int32.
Assert that the schemas of actual and expected are equal.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Match the dtype the pipeline produces
expected = expected.with_columns(
    pl.col("total_checkouts").cast(pl.____)
)

# Schema-level assertion
____(actual.schema, expected.schema)
print("Schema assertion passed.")
print(actual.schema)

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