pandera.decorators.check_types#
- pandera.decorators.check_types(wrapped: pandera.decorators.F, *, with_pydantic: bool = 'False', head: Optional[int] = 'None', tail: Optional[int] = 'None', sample: Optional[int] = 'None', random_state: Optional[int] = 'None', lazy: bool = 'False', inplace: bool = 'False') pandera.decorators.F [source]#
- pandera.decorators.check_types(wrapped: None = None, *, with_pydantic: bool = 'False', head: Optional[int] = 'None', tail: Optional[int] = 'None', sample: Optional[int] = 'None', random_state: Optional[int] = 'None', lazy: bool = 'False', inplace: bool = 'False') Callable[[pandera.decorators.F], pandera.decorators.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
) – usepydantic.validate_arguments
to validate inputs. This function is still needed to validate function outputs.head (
Optional
[int
]) – validate the first n rows. Rows overlapping with tail or sample are de-duplicated.tail (
Optional
[int
]) – validate the last n rows. Rows overlapping with head or sample are de-duplicated.sample (
Optional
[int
]) – validate a random sample of n rows. Rows overlapping with head or tail are de-duplicated.random_state (
Optional
[int
]) – random seed for thesample
argument.lazy (
bool
) – if True, lazily evaluates dataframe against all validation checks and raises aSchemaErrors
. Otherwise, raiseSchemaError
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