nannyml.distribution.categorical.result module

class nannyml.distribution.categorical.result.Result(results_data: DataFrame, column_names: List[str], timestamp_column_name: Optional[str], chunker: Chunker)[source]

Bases: AbstractResult

Creates a new AbstractCalculatorResult instance.

Parameters:: results_data (pd.DataFrame) – The data returned by the Calculator.

check_is_compatible_with(drift_result: Result)[source]

property chunk_end_dates: Series

property chunk_end_indices: Series

property chunk_indices: Series

property chunk_keys: Series

property chunk_periods: Series

property chunk_start_dates: Series

property chunk_start_indices: Series

keys() → List[Key][source]

plot(drift_result: Optional[Result] = None, *args, **kwargs) → Figure[source]

Creates a “joyplot over time” visualization to illustrate continuous distribution changes over time.

Parameters:: drift_result (Optional[nannyml.drift.univariate.Result]) – The result of a univariate drift calculation. When set it will be used to lookup alerts that occurred for each column / drift method combination in the drift calculation result. For each of these combinations a distribution plot of the column will be rendered showing the alerts for each drift method. When the drift_result parameter is not set no alerts will be rendered on the distribution plots.

to_df(multilevel: bool = True) → DataFrame[source]: Export results to pandas dataframe.

value_counts(key: Optional[Key] = None, column_name: Optional[str] = None) → DataFrame[source]