# Check the Schema of a Table
Pointblank Validation
2026-07-13|17:45:14 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		 -- -- -- --
2026-07-13 17:45:14 UTC< 1 s2026-07-13 17:45:14 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
``` python 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 ```