ParallelCategoriesExplorer
Visualise categorical data flows across multiple dimensions simultaneously.
A parallel categories diagram represents each row of the dataset as a ribbon flowing through a series of vertical axes, one per selected column. The width of each ribbon is proportional to the number of samples that share that combination of categories. An optional colour axis further segments the flows by a continuous or categorical variable, making patterns of co-occurrence and class distribution immediately visible.
Best suited for exploring relationships between three or more categorical columns, such as demographic cross-tabulations or multi-label feature analysis.
Parameters
- color_column, default=
None - Column used to color the data points.
Methods
get_results(self, exploration_path: str, options: Dict[str, Any]) -> Dict[str, Any]
ParallelCategoriesExplorerLoad and return the saved parallel categories 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)
ParallelCategoriesExplorerGenerate a Plotly parallel categories plot for the selected columns.
Parameters
- dataset : DashAIDataset
- The prepared dataset with at least two columns.
- explorer_info : Explorer
- Explorer record with column names and optional display name.
Returns
- plotly.graph_objects.Figure
- An interactive parallel categories figure.
prepare_dataset(self, loaded_dataset: 'DashAIDataset', columns: List[Dict[str, Any]]) -> 'DashAIDataset'
ParallelCategoriesExplorerExtend column selection to include the optional color column.
Parameters
- loaded_dataset : DashAIDataset
- The full dataset.
- columns : List[Dict[str, Any]]
- Explicitly selected column descriptors.
Returns
- DashAIDataset
- Dataset containing the selected columns plus the optional color column.
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
ParallelCategoriesExplorerSave the parallel categories figure to a JSON file on disk.
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 JSON 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.