MixedNulls#

class MixedNulls[source]#

Search for various types of null values, including string representations of null.

Parameters
null_string_listIterable[str] , default: None

List of strings to be considered alternative null representations

check_nanbool , default: True

Whether to add to null list to check also NaN values

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__(null_string_list: Optional[Iterable[str]] = None, check_nan: bool = True, columns: Optional[Union[Hashable, List[Hashable]]] = None, ignore_columns: Optional[Union[Hashable, List[Hashable]]] = None, n_top_columns: int = 10, **kwargs)[source]#
__new__(*args, **kwargs)#

Methods

MixedNulls.add_condition(name, ...)

Add new condition function to the check.

MixedNulls.add_condition_different_nulls_not_more_than([...])

Add condition - require column not to have more than given number of different null values.

MixedNulls.clean_conditions()

Remove all conditions from this check instance.

MixedNulls.conditions_decision(result)

Run conditions on given result.

MixedNulls.finalize_check_result(check_result)

Finalize the check result by adding the check instance and processing the conditions.

MixedNulls.metadata([with_doc_link])

Return check metadata.

MixedNulls.name()

Name of class in split camel case.

MixedNulls.params([show_defaults])

Return parameters to show when printing the check.

MixedNulls.remove_condition(index)

Remove given condition by index.

MixedNulls.run(dataset[, model])

Run check.

MixedNulls.run_logic(context[, dataset_type])

Run check.

Examples#