MixedDataTypes

class MixedDataTypes

Detect columns which contain a mix of numerical and string values.

Parameters

columnsUnion[Hashable, List[Hashable]] , default: None

Columns to check, if none are given checks all columns except ignored ones.

ignore_columnsUnion[Hashable, List[Hashable]] , default: None

Columns to ignore, if none given checks based on columns variable.

n_top_columnsint , optional

amount of columns to show ordered by feature importance (date, index, label are first)

__init__(columns: Optional[Union[Hashable, List[Hashable]]] = None, ignore_columns: Optional[Union[Hashable, List[Hashable]]] = None, n_top_columns: int = 10, **kwargs)

__new__(*args, **kwargs)

Methods

MixedDataTypes.add_condition(name, ...)	Add new condition function to the check.
MixedDataTypes.add_condition_rare_type_ratio_not_in_range([...])	Add condition - Whether the ratio of rarer data type (strings or numbers) is not in the "danger zone".
MixedDataTypes.clean_conditions()	Remove all conditions from this check instance.
MixedDataTypes.conditions_decision(result)	Run conditions on given result.
MixedDataTypes.config()	Return check configuration (conditions' configuration not yet supported).
MixedDataTypes.from_config(conf)	Return check object from a CheckConfig object.
MixedDataTypes.metadata([with_doc_link])	Return check metadata.
MixedDataTypes.name()	Name of class in split camel case.
MixedDataTypes.params([show_defaults])	Return parameters to show when printing the check.
MixedDataTypes.remove_condition(index)	Remove given condition by index.
MixedDataTypes.run(dataset[, model, ...])	Run check.
MixedDataTypes.run_logic(context, dataset_kind)	Run check.

Examples

Mixed Data Types

previous

StringMismatch.run_logic

next

MixedDataTypes.add_condition