gooddata_sdk.table.ExecutionTable

class gooddata_sdk.table.ExecutionTable(response: Execution, 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

Methods

`__init__`(response, first_page)
`read_all`()	Returns a generator that will be yielding execution result as rows.

Attributes

`attributes`
`column_ids`	Returns column identifiers.
`column_metadata`	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.

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.

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.