gooddata_sdk.table.ExecutionTable
- class gooddata_sdk.table.ExecutionTable(response: ExecutionResponse, first_page: ExecutionResult)
Bases:
object
Represents execution result as a table. This is a convenience wrapper for executions constructed using the following convention:
all attributes are in the first dimension
all metrics are in the second dimension
if the execution is attribute- or metric-less, then there is always single dimension
The mapping to rows is then as follows:
both attributes + metrics are on the execution = iteration over first dimension; as many rows as total records in the first dimension (paging.total[0])
just attributes = iteration over just headers in first dimension; as many rows as total records in the first dimension (paging.total[0])
just metrics = single row, all metrics values returned in one row
- __init__(response: ExecutionResponse, first_page: ExecutionResult) None
Methods
__init__
(response, first_page)read_all
()Returns a generator that will be yielding execution result as rows.
Attributes
attributes
Returns column identifiers.
Returns mapping of column identifier to definition of either attribute whose elements will be in that column or metric whose value will be calculated in that column.
metrics
- property column_ids: list[str]
Returns column identifiers. Each row will be a mapping of column identifier to column data.
- Returns
- property column_metadata: dict[str, Union[Attribute, Metric]]
Returns mapping of column identifier to definition of either attribute whose elements will be in that column or metric whose value will be calculated in that column. :return:
- read_all() Generator[dict[str, Any], None, None]
Returns a generator that will be yielding execution result as rows. Each row is a dict() mapping column identifier to value of that column.
- Returns
generator yielding dict() representing rows of the table