Error Reporting

Pandera raises SchemaError for individual validation failures and SchemaErrors when lazy=True.

Non-lazy validation (fail-fast)

By default, validation stops on the first error:

import torch
from tensordict import TensorDict
import pandera.tensordict as pa

schema = pa.TensorDictSchema(
    keys={"x": pa.Tensor(dtype=torch.float32, shape=(None, 10))},
    batch_size=(32,),
)

td = TensorDict({"x": torch.randn(16, 10)}, batch_size=[16])

try:
    schema.validate(td)
except pa.errors.SchemaError as exc:
    print(f"Error: {exc}")
    print(f"Reason: {exc.reason_code}")

Error: Expected batch_size[0]=32, got batch_size[0]=16
Reason: SchemaErrorReason.WRONG_DATATYPE

Lazy validation

Set lazy=True to collect all errors:

td_invalid = TensorDict(
    {"x": torch.randn(16, 10), "y": torch.randn(16, 10)},
    batch_size=[16],
)

try:
    schema.validate(td_invalid, lazy=True)
except pa.errors.SchemaErrors as exc:
    print(f"Found {len(exc.schema_errors)} errors")
    for err in exc.schema_errors:
        print(f"  - {str(err)}")
    print(f"\nError counts: {exc.error_counts}")

Found 1 errors
  - Expected batch_size[0]=32, got batch_size[0]=16

Error counts: {'WRONG_DATATYPE': 1}

Error reason codes

  • WRONG_DATATYPE — tensor dtype mismatch

  • COLUMN_NOT_IN_DATAFRAME — missing key

  • CHECK_ERROR — value check failure

See also