nannyml.data_quality.missing.result module
Contains the results of the univariate statistical drift calculation and provides plotting functionality.
- class nannyml.data_quality.missing.result.Result(results_data: pandas.core.frame.DataFrame, column_names: List[str], data_quality_metric: str, timestamp_column_name: Optional[str], chunker: nannyml.chunk.Chunker)[source]
Bases:
nannyml.base.PerColumnResult
,nannyml.plots.blueprints.comparisons.ResultCompareMixin
Contains the results of the univariate statistical drift calculation and provides plotting functionality.
Creates a new
AbstractCalculatorResult
instance.- Parameters
results_data (pd.DataFrame) – The data returned by the Calculator.
- plot(*args, **kwargs) plotly.graph_objs._figure.Figure [source]
- Returns
fig – A
Figure
object containing the requested drift plot.Can be saved to disk using the
write_image()
method or shown rendered on screen using theshow()
method.- Return type
plotly.graph_objs._figure.Figure
Examples
>>> import nannyml as nml >>> reference, analysis, _ = nml.load_synthetic_car_price_dataset() >>> column_names = [col for col in reference.columns if col not in ['timestamp', 'y_pred', 'y_true']] >>> calc = nml.MissingValuesCalculator( ... column_names=column_names, ... timestamp_column_name='timestamp', ... ).fit(reference) >>> res = calc.calculate(analysis) >>> for column_name in res.column_names: ... res = res.filter(period='analysis', column_name=column_name).plot().show()