for_exec_def
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"]) ->
tuple[pandas.DataFrame, DataFrameMetadata]
Creates a data frame using an execution definition.
Each dimension may be sliced by multiple labels. The factory will create MultiIndex for the dataframe's row index and the columns. Example of label_overrides structure: .. code-block:: python { "labels": { "local_attribute_id": { "title": "My new attribute label" ,... }, "metrics": { "local_metric_id": { "title": "My new metric label" },... } }
Parameters
| name | type | description |
|---|---|---|
| exec_def | ExecutionDefinition | Execution definition. |
| label_overrides | Optional[LabelOverrides] | Label overrides for metrics and attributes. |
| result_size_dimensions_limits | ResultSizeDimensions | A tuple containing maximum size of result dimensions. |
| result_size_bytes_limit | Optional[int] | Maximum size of result in bytes. |
| page_size | int | Number of records per page. |
| on_execution_submitted | Optional[Callable[[Execution], None]] | Callback to call when the execution was submitted to the backend. |
| optimized | bool, default=False | Use memory optimized accumulator if True; by default, the accumulator stores headers in memory as lists of dicts, which can consume a lot of memory for large results. Optimized accumulator stores only unique values and story only reference to them in the list, which can significantly reduce memory usage. |
| grand_totals_position | Literal["pinnedBottom", "pinnedTop", "bottom", "top"] | Position where grand totals should be placed. "pinnedBottom" and "bottom" append totals, "pinnedTop" and "top" prepend totals. Defaults to "bottom". |
Returns
| type | description |
|---|---|
| Tuple[pandas.DataFrame, DataFrameMetadata] | Tuple holding DataFrame and DataFrame metadata. |