MultiColumnBoxPlotExplorer
Explorer that renders one box plot trace per selected column on a shared figure.
While the single-column BoxPlotExplorer is suited to examining one variable at a time, this explorer places multiple box plot traces side by side in the same figure, making it straightforward to compare the distributional properties — median, spread, and outliers — of several numeric columns simultaneously.
Each box trace summarises its column through the five-number summary (Q1,
median, Q3, lower whisker, upper whisker) and optionally overlays individual
data points. When an opposite_axis column is provided, every trace is
additionally split by the distinct categories of that column, producing
grouped boxes that reveal how the distribution of each numeric column varies
across groups.
Use this explorer when you need a compact, side-by-side comparison of multiple numeric features, for example to detect scale differences between model input features or to contrast the spread of several metrics across experimental conditions.
Parameters
- horizontal : boolean, default=
False - If True, the box plot will be horizontal; otherwise vertical.
- points : string, default=
outliers - One of 'all', 'outliers', or 'False'. Determines which points are shown.
- opposite_axis, default=
None - Column name or index to use for the opposite axis.
Methods
get_results(self, exploration_path: str, options: Dict[str, Any]) -> Dict[str, Any]
MultiColumnBoxPlotExplorerLoad and return the saved multi-column box plot for the frontend.
Parameters
- exploration_path : str
- Path to the JSON file saved by
save_notebook. - options : Dict[str, Any]
- Rendering options from the frontend (unused).
Returns
- Dict[str, Any]
- Dictionary with keys
"data"(JSON-serialized Plotly figure),"type"("plotly_json"), and"config"(empty dict).
launch_exploration(self, dataset: 'DashAIDataset', explorer_info: DashAI.back.dependencies.database.models.Explorer)
MultiColumnBoxPlotExplorerGenerate a Plotly figure with a box plot trace for each selected column.
Parameters
- dataset : DashAIDataset
- Dataset containing the selected columns and, if configured, the opposite-axis column.
- explorer_info : Explorer
- Explorer record with column names and optional display name.
Returns
- plotly.graph_objects.Figure
- An interactive multi-box plot figure with one box trace per selected column.
prepare_dataset(self, loaded_dataset: 'DashAIDataset', columns: List[Dict[str, Any]]) -> 'DashAIDataset'
MultiColumnBoxPlotExplorerExtend the column list to include the opposite-axis column if specified.
Parameters
- loaded_dataset : DashAIDataset
- The full dataset being explored.
- columns : List[Dict[str, Any]]
- List of column descriptors already selected by the user.
Returns
- DashAIDataset
- Dataset slice containing all required columns, as returned by the parent
prepare_datasetimplementation.
save_notebook(self, notebook_info: DashAI.back.dependencies.database.models.Notebook, explorer_info: DashAI.back.dependencies.database.models.Explorer, save_path: 'Path', result: Any) -> str
MultiColumnBoxPlotExplorerSave the multi-column box plot figure to disk (JSON content, .pickle extension).
Parameters
- notebook_info : Notebook
- The notebook database record (unused).
- explorer_info : Explorer
- The explorer record used for filename generation.
- save_path : Path
- Directory where the file will be saved.
- result : Any
- The Plotly figure returned by
launch_exploration.
Returns
- str
- The path of the saved file as a POSIX string.
get_metadata(cls) -> Dict[str, Any]
BaseExplorerGet metadata for the explorer, used by the DashAI frontend.
Returns
- Dict[str, Any]
- Dictionary containing display name, description, image preview path, category, icon, color, allowed dtypes, restricted dtypes, and input cardinality constraints.
get_schema(cls) -> dict
ConfigObjectGenerates the component related Json Schema.
Returns
- dict
- Dictionary representing the Json Schema of the component.
validate_and_transform(self, raw_data: dict) -> dict
ConfigObjectIt takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.
Parameters
- raw_data : dict
- A dictionary with the data provided by the user to initialize the model.
Returns
- dict
- A validated dictionary with the necessary objects.
validate_columns(cls, explorer_info: DashAI.back.dependencies.database.models.Explorer, column_spec: Dict[str, Dict[str, str]]) -> bool
BaseExplorerValidate that the selected columns satisfy the explorer's constraints.
Parameters
- explorer_info : Explorer
- The database record for the explorer instance, including the selected columns.
- column_spec : Dict[str, Dict[str, str]]
- A mapping from column name to a dict with at least a
"type"key describing the column's data type.
Returns
- bool
- True if all column constraints are satisfied, False otherwise.
validate_parameters(cls, params: Dict[str, Any]) -> bool
BaseExplorerValidate explorer parameters against the explorer's schema.
Parameters
- params : Dict[str, Any]
- The configuration parameters to validate.
Returns
- BaseExplorerSchema
- The validated and parsed schema instance. Subclasses that override this method may return a bool to indicate pass/fail without returning the model instance.