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) -> 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.

Returns

type	description
Tuple[pandas.DataFrame, DataFrameMetadata]	Tuple holding DataFrame and DataFrame metadata.

for_created_visualization

for_exec_result_id