StringMismatch

class StringMismatch

Detect different variants of string categories (e.g. “mislabeled” vs “mis-labeled”) in a categorical column.

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)

__new__(*args, **kwargs)

Methods

StringMismatch.add_condition(name, ...)	Add new condition function to the check.
StringMismatch.add_condition_no_variants()	Add condition - no variants are allowed.
StringMismatch.add_condition_not_more_variants_than(...)	Add condition - no more than given number of variants are allowed (per string baseform).
StringMismatch.add_condition_ratio_variants_not_greater_than([...])	Add condition - percentage of variants in data is not allowed above given threshold.
StringMismatch.clean_conditions()	Remove all conditions from this check instance.
StringMismatch.conditions_decision(result)	Run conditions on given result.
StringMismatch.name()	Name of class in split camel case.
StringMismatch.params([show_defaults])	Return parameters to show when printing the check.
StringMismatch.remove_condition(index)	Remove given condition by index.
StringMismatch.run(dataset[, model])	Run check.
StringMismatch.run_logic(context[, dataset_type])	Run check.

Example

StringMismatch Example Notebook

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MixedNulls.run_logic

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StringMismatch.add_condition