ScatterPlotExplorer
Display a two-dimensional scatter plot to explore relationships between columns.
Renders each dataset row as a point in a 2-D plane defined by two selected numeric columns. Additional visual channels (colour, marker symbol, point size) can be mapped to further columns to reveal clustering, class separation, or a third quantitative dimension without requiring a higher-dimensional plot.
A scatter plot is the primary tool for detecting linear and non-linear correlations between two variables and for spotting outliers, heteroscedasticity, or discrete groupings in the joint distribution.
Parameters
- color_group, default=
None - Column name or index used to group colored points.
- simbol_group, default=
None - Column name or index used to group point symbols.
- point_size, default=
None - Column name or index to set the size of each point.
Methods
get_results(self, exploration_path: str, options: Dict[str, Any]) -> Dict[str, Any]
ScatterPlotExplorerLoad and return the saved scatter 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)
ScatterPlotExplorerGenerate a Plotly scatter plot for two selected columns.
Parameters
- dataset : DashAIDataset
- The prepared dataset with the required columns.
- explorer_info : Explorer
- Explorer record with column names and optional display name.
Returns
- plotly.graph_objects.Figure
- An interactive scatter plot figure.
prepare_dataset(self, loaded_dataset: 'DashAIDataset', columns: List[Dict[str, Any]]) -> 'DashAIDataset'
ScatterPlotExplorerExtend column selection to include optional grouping columns.
Parameters
- loaded_dataset : DashAIDataset
- The full dataset.
- columns : List[Dict[str, Any]]
- Explicitly selected column descriptors.
Returns
- DashAIDataset
- Dataset containing the selected columns plus any optional grouping columns.
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
ScatterPlotExplorerSave the scatter 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.