pandera.api.dataframe.model.DataFrameModel

class pandera.api.dataframe.model.DataFrameModel(*args, **kwargs)

Base class for the DataFrame model.

See the User Guide for more.

Validate a DataFrame based on the schema specification.

Parameters:

• check_obj (pd.DataFrame) – the dataframe to be validated.

• head – validate the first n rows. Rows overlapping with tail or sample are de-duplicated.

• tail – validate the last n rows. Rows overlapping with head or sample are de-duplicated.

• sample – validate a random sample of n rows. Rows overlapping with head or tail are de-duplicated.

• random_state – random seed for the sample argument.

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

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

Returns:

validated DataFrame

Raises:

SchemaError – when DataFrame violates built-in or custom checks.

Methods

classmethod build_schema_(**kwargs)

Return type:

~TSchema

classmethod empty(*_args)

Create an empty DataFrame instance.

Return type:

DataFrame[~TDataFrameModel]

classmethod example(cls, **kwargs)

Generate an example of a particular size.

Parameters:

size – number of elements in the generated DataFrame.

Return type:

DataFrameBase[~TDataFrameModel]

Returns:

DataFrame object.

classmethod get_metadata()

Provide metadata for columns and schema level

Return type:

Optional[dict]

classmethod pydantic_validate(schema_model)

Verify that the input is a compatible dataframe model.

Return type:

DataFrameModel

classmethod strategy(cls, **kwargs)

Create a hypothesis strategy for generating a DataFrame.

Parameters:

• size – number of elements to generate

• n_regex_columns – number of regex columns to generate.

Returns:

a strategy that generates DataFrame objects.

classmethod to_json_schema()

Serialize schema metadata into json-schema format.

classmethod to_schema()

Create DataFrameSchema from the DataFrameModel.

Return type:

~TSchema

classmethod to_yaml(stream=None)

Convert Schema to yaml using io.to_yaml.

classmethod validate(check_obj, head=None, tail=None, sample=None, random_state=None, lazy=False, inplace=False)

Validate a DataFrame based on the schema specification.

Parameters:

• check_obj (pd.DataFrame) – the dataframe to be validated.

• 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 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:

DataFrameBase[~TDataFrameModel]

Returns:

validated DataFrame

Raises:

SchemaError – when DataFrame violates built-in or custom checks.