pandera.decorators.check_types

pandera.decorators.check_types(wrapped: F, *, with_pydantic: bool = False, head: int | None = None, tail: int | None = None, sample: int | None = None, random_state: int | None = None, lazy: bool = False, inplace: bool = False) → F[source]
pandera.decorators.check_types(wrapped: None = None, *, with_pydantic: bool = False, head: int | None = None, tail: int | None = None, sample: int | None = None, random_state: int | None = None, lazy: bool = False, inplace: bool = False) → Callable[[F], F]

Validate function inputs and output based on type annotations.

See the User Guide for more.

Parameters:

  • wrapped – the function to decorate.

  • with_pydantic (bool) – use pydantic.validate_arguments to validate inputs. This function is still needed to validate function outputs.

  • head (Optional[int, None]) – validate the first n rows. Rows overlapping with tail or sample are de-duplicated.

  • tail (Optional[int, None]) – validate the last n rows. Rows overlapping with head or sample are de-duplicated.

  • sample (Optional[int, None]) – validate a random sample of n rows. Rows overlapping with head or tail are de-duplicated.

  • random_state (Optional[int, None]) – random seed for the sample argument.

  • lazy (bool) – if True, lazily evaluates dataframe against all validation checks and raises a SchemaErrors. Otherwise, raise SchemaError as soon as one occurs.

  • inplace (bool) – if True, applies coercion to the object of validation, otherwise creates a copy of the data.

Return type:

Callable