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dataframe.DataFrameFactory
Factory to create pandas.DataFrame instances.
Properties
NoneMethods
| Name | Description |
|---|---|
| indexed(index_by: IndexDef, columns: ColumnsDef, filter_by: Optional[Union[Filter, list[Filter]]], on_execution_submitted: Optional[Callable[[Execution], None]], is_cancellable: bool, result_page_len: Optional[int]) | Creates a data frame indexed by values of the label. The data frame columns will be created from either metrics or other label values. |
| not_indexed(columns: ColumnsDef, filter_by: Optional[Union[Filter, list[Filter]]], on_execution_submitted: Optional[Callable[[Execution], None]], is_cancellable: bool, result_page_len: Optional[int]) | Creates a data frame with columns created from metrics and or labels. |
| for_items(items: ColumnsDef, filter_by: Optional[Union[Filter, list[Filter]]], auto_index: bool, on_execution_submitted: Optional[Callable[[Execution], None]], is_cancellable: bool, result_page_len: Optional[int]) | Creates a data frame for named items. This is a convenience method that will create DataFrame with or without an index based on the context of the items that you pass. |
| for_visualization(visualization_id: str, auto_index: bool, on_execution_submitted: Optional[Callable[[Execution], None]], is_cancellable: bool, result_page_len: Optional[int]) | Creates a data frame with columns based on the content of the visualization with the provided identifier. |
| for_created_visualization(created_visualizations_response: dict, on_execution_submitted: Optional[Callable[[Execution], None]], is_cancellable: bool, optimized: bool, default=False, grand_totals_position: Literal['pinnedBottom', 'pinnedTop', 'bottom', 'top']) | Creates a data frame using a created visualization. |
| result_cache_metadata_for_exec_result_id(result_id: str) | Retrieves result cache metadata for given :result_id: |
| for_exec_def(exec_def: ExecutionDefinition, label_overrides: Optional[LabelOverrides], result_size_dimensions_limits: ResultSizeDimensions, result_size_bytes_limit: Optional[int], page_size: int, on_execution_submitted: Optional[Callable[[Execution], None]], optimized: bool, default=False, grand_totals_position: Literal['pinnedBottom', 'pinnedTop', 'bottom', 'top']) | Creates a data frame using an execution definition. |
| for_exec_def_arrow(exec_def: ExecutionDefinition, label_overrides: Optional[LabelOverrides], grand_totals_position: Literal['pinnedBottom', 'pinnedTop', 'bottom', 'top'], on_execution_submitted: Optional[Callable[[Execution], None]]) | Creates a DataFrame from an execution definition using the Arrow IPC binary format. |
| for_arrow_table(table: pa.Table, execution_response: BareExecutionResponse | None, label_overrides: Optional[LabelOverrides], grand_totals_position: Literal['pinnedBottom', 'pinnedTop', 'bottom', 'top']) | Creates a DataFrame from an already-obtained PyArrow Table. |
| for_exec_result_id(result_id: str, label_overrides: Optional[LabelOverrides], result_cache_metadata: Optional[ResultCacheMetadata], result_size_dimensions_limits: ResultSizeDimensions, result_size_bytes_limit: Optional[int], use_local_ids_in_headers: bool, use_primary_labels_in_attributes: bool, page_size: int, optimized: bool, default=False, grand_totals_position: Literal['pinnedBottom', 'pinnedTop', 'bottom', 'top']) | Retrieves a DataFrame and DataFrame metadata for a given execution result identifier. |