Check the Schema of a Table

The schema of a table can be flexibly defined with Schema and verified with col_schema_match().

Pointblank Validation
2025-12-01|23:51:09 Polars
		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	E	C	EXT
#4CA64C	1	col_schema_match()	—	SCHEMA		✓	1	1 1.00	0 0.00	—	—	—	—
2025-12-01 23:51:09 UTC< 1 s2025-12-01 23:51:09 UTC
Notes Step 1 (schema_check) ✓ Schema validation passed. Schema Comparison TARGET EXPECTED COLUMN DATA TYPE COLUMN DATA TYPE 1 a String 1 a ✓ String ✓ 2 b Int64 2 b ✓ Int | Int64 ✓ 3 c Float64 3 c ✓ — Supplied Column Schema: [('a', 'String'), ('b', ['Int', 'Int64']), ('c',)] Schema Match Settings COMPLETE IN ORDER COLUMN ≠ column DTYPE ≠ dtype float ≠ float64

import pointblank as pb
import polars as pl

tbl = pl.DataFrame(
    {
        "a": ["apple", "banana", "cherry", "date"],
        "b": [1, 6, 3, 5],
        "c": [1.1, 2.2, 3.3, 4.4],
    }
)

# Use the Schema class to define the column schema as loosely or rigorously as required
schema = pb.Schema(
    columns=[
        ("a", "String"),          # Column 'a' has dtype 'String'
        ("b", ["Int", "Int64"]),  # Column 'b' has dtype 'Int' or 'Int64'
        ("c", )                   # Column 'c' follows 'b' but we don't specify a dtype here
    ]
)

# Use the `col_schema_match()` validation method to perform the schema check
validation = (
    pb.Validate(data=tbl)
    .col_schema_match(schema=schema)
    .interrogate()
)

validation