pandera.decorators.check_io#
- pandera.decorators.check_io(head=None, tail=None, sample=None, random_state=None, lazy=False, inplace=False, out=None, **inputs)[source]#
Check schema for multiple inputs and outputs.
See here for more usage details.
- Parameters
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.out (
Union
[DataFrameSchema
,SeriesSchema
,Tuple
[Union
[str
,int
,Callable
],Union
[DataFrameSchema
,SeriesSchema
]],List
[Tuple
[Union
[str
,int
,Callable
],Union
[DataFrameSchema
,SeriesSchema
]]],None
]) – this should be a schema object if the function outputs a single dataframe/series. It can be a two-tuple, where the first element is a string, integer, or callable that fetches the pandas data structure in the output, and the second element is the schema to validate against. For multiple outputs, specify a list of two-tuples following the above structure.inputs (
Union
[DataFrameSchema
,SeriesSchema
]) – kwargs keys should be the argument name in the decorated function and values should be the schema used to validate the pandas data structure referenced by the argument name.
- Return type
Callable
[[~F], ~F]- Returns
wrapped function