RowExplorer
Display a tabular sample of dataset rows for direct data inspection.
Returns up to row_ammount rows from the dataset rendered as a table.
Rows can be taken from the head or tail of the dataset, and optionally
shuffled before sampling to obtain a random preview. This is typically the
first exploration step after loading a dataset, allowing users to verify
column types, spot obvious data quality issues, and understand the raw
data format before applying transformations.
Parameters
- row_ammount : integer, default=
50 - Maximum number of rows to take.
- shuffle : boolean, default=
False - Shuffle the rows when exploring.
- from_top : boolean, default=
True - Take rows from the head of the dataset. Otherwise, take from the tail.
Methods
get_results(self, exploration_path: str, options: Dict[str, Any]) -> Dict[str, Any]
RowExplorerLoad and return the saved row sample 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. Supports
"orientation"(str, default"dict"), which is forwarded topandas.DataFrame.to_dict.
Returns
- Dict[str, Any]
- Dictionary with keys
"data"(nested dict of the sampled rows in the requested orientation),"type"("tabular"), and"config"(dict containing{"orient": <orientation>}).
launch_exploration(self, dataset: 'DashAIDataset', explorer_info: DashAI.back.dependencies.database.models.Explorer)
RowExplorerReturn a subset of dataset rows for tabular preview.
Parameters
- dataset : DashAIDataset
- The dataset to sample rows from.
- explorer_info : Explorer
- The explorer database record (unused).
Returns
- Any
- A
pandas.DataFramecontaining at mostself.row_ammountrows selected from the head or tail of the (optionally shuffled) dataset.
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
RowExplorerSave the sampled rows DataFrame 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
pandas.DataFramereturned bylaunch_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.
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.