WordcloudExplorer
Visualise word frequency in text columns as a word cloud.
Concatenates the values of all selected text columns across every row of the dataset and produces a word cloud image where each word is rendered at a size proportional to its term frequency. Stop words are not removed automatically; pre-processing should be applied via converters before running this explorer if stop-word filtering is desired.
Word clouds are a quick way to identify the most common terms in a text corpus, detect vocabulary overlap between classes, and communicate the dominant topics in a dataset to non-technical audiences.
Parameters
- max_words : integer, default=
200 - Maximum number of words to display in the word cloud.
- background_color, default=
None - Background color of the word cloud. If None, the background is transparent.
Methods
get_results(self, exploration_path: str, options: Dict[str, Any]) -> Dict[str, Any]
WordcloudExplorerLoad and return the saved word cloud image for the frontend.
Parameters
- exploration_path : str
- Path to the PNG file saved by
save_notebook. - options : Dict[str, Any]
- Rendering options from the frontend (unused).
Returns
- Dict[str, Any]
- Dictionary with keys
"data"(base64-encoded UTF-8 string of the PNG image),"type"("image_base64"), and"config"(empty dict).
launch_exploration(self, dataset: 'DashAIDataset', explorer_info: DashAI.back.dependencies.database.models.Explorer)
WordcloudExplorerGenerate a word cloud image from the selected text columns.
Parameters
- dataset : DashAIDataset
- The dataset containing the text columns.
- explorer_info : Explorer
- The explorer database record used to determine which columns to concatenate.
Returns
- Any
- A
PIL.Image.Imageof the rendered word cloud (PNG-compatible, RGBA whenbackground_colorisNone, RGB otherwise).
save_notebook(self, exploration_info: DashAI.back.dependencies.database.models.Notebook, explorer_info: DashAI.back.dependencies.database.models.Explorer, save_path: 'Path', result: Any) -> str
WordcloudExplorerSave the word cloud PIL image to a PNG file on disk.
Parameters
- exploration_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
PIL.Image.Imagereturned bylaunch_exploration.
Returns
- str
- The path of the saved PNG 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.
prepare_dataset(self, loaded_dataset: 'DashAIDataset', columns: List[Dict[str, Any]]) -> 'DashAIDataset'
BaseExplorerPrepare the dataset by selecting only the columns needed for this exploration.
Parameters
- loaded_dataset : DashAIDataset
- The full dataset loaded from storage.
- columns : List[Dict[str, Any]]
- List of column descriptor dicts, each containing at least
"columnName". Optional keys:"id","valueType","dataType".
Returns
- DashAIDataset
- Dataset restricted to the requested columns.
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.