gooddata_pandas.series.SeriesFactory

class gooddata_pandas.series.SeriesFactory(sdk: GoodDataSdk, workspace_id: str)

Bases: object

This class serves as a factory to create Series objects by providing the necessary parameters.

Attributes:

sdk (GoodDataSdk): An instance of the GoodData software development kit. workspace_id (str): The unique identifier of the workspace.

__init__(sdk: GoodDataSdk, workspace_id: str) → None

Methods

__init__(sdk, workspace_id)

indexed(index_by, data_by[, filter_by])

Creates pandas Series from data points calculated from a single data_by.

not_indexed(data_by[, granularity, filter_by])

Creates a pandas.Series from data points calculated from a single data_by without constructing an index.
indexed(index_by: Union[Attribute, ObjId, str, Dict[str, Union[Attribute, ObjId, str]]], data_by: Union[SimpleMetric, str, ObjId, Attribute], filter_by: Optional[Union[Filter, list[gooddata_sdk.compute.model.base.Filter]]] = None) → Series

Creates pandas Series from data points calculated from a single data_by.

Creates pandas Series from data points calculated from a single data_by, that will be computed on granularity of the index labels. The elements of the index labels will be used to construct simple or hierarchical index.

Args:

index_by (IndexDef): label to index by; specify either:

  • string with id: some_label_id,

  • object identifier: ObjId(id='some_label_id', type='label'),

  • string representation of object identifier: label/some_label_id

  • or an Attribute object used in the compute model: Attribute(local_id=..., label='some_label_id')

  • dict containing mapping of index name to label to use for indexing (in one of the ways listed above)

data_by (Union[SimpleMetric, str, ObjId, Attribute]): label, fact or metric to that will provide data

(metric values or label elements); specify either:

  • object identifier: ObjId(id='some_id', type='<type>') - where type is either label, fact or metric

  • string representation of object identifier: <type>/some_id - where type is either label, fact or metric

  • Attribute object used in the compute model: Attribute(local_id=..., label='some_label_id')

  • SimpleMetric object used in the compute model: SimpleMetric(local_id=..., item=..., aggregation=...)

filter_by (Optional[Union[Filter, list[Filter]]]): optionally specify filter to apply during computation on the server, reference to filtering column can be one of:

  • string reference to index key

  • object identifier in string form

  • object identifier: ObjId(id='some_label_id', type='<type>')

  • Attribute or Metric depending on type of filter

Returns:

pandas.Series: pandas series instance

not_indexed(data_by: Union[SimpleMetric, str, ObjId, Attribute], granularity: Optional[Union[list[Union[gooddata_sdk.compute.model.attribute.Attribute, gooddata_sdk.compute.model.base.ObjId, str]], Attribute, ObjId, str, Dict[str, Union[Attribute, ObjId, str]]]] = None, filter_by: Optional[Union[Filter, list[gooddata_sdk.compute.model.base.Filter]]] = None) → Series

Creates a pandas.Series from data points calculated from a single data_by without constructing an index.

Args:
data_by (Union[SimpleMetric, str, ObjId, Attribute]): The label, fact, or metric to obtain data from.
Specify either:

  • ObjId: ObjId(id=’some_id’, type=’<type>’)

  • string representation of an identifier: ‘<type>/some_id’

  • Attribute: Attribute(local_id=…, label=’some_label_id’)

  • SimpleMetric: SimpleMetric(local_id=…, item=…, aggregation=…)

granularity (Optional[Union[list[LabelItemDef], IndexDef]], optional): The label to slice the metric by.
Specify either:

  • string with id: ‘some_label_id’

  • ObjId: ObjId(id=’some_label_id’, type=’label’)

  • string representation of an identifier: ‘label/some_label_id’

  • Attribute: Attribute(local_id=…, label=’some_label_id’)

  • list containing multiple labels to slice the metric by

  • dict containing mapping of index name to label

Defaults to None.

filter_by (Optional[Union[Filter, list[Filter]]], optional): The filter(s) to apply. Reference to filtering
column can be one of:

  • object identifier in string form

  • ObjId: ObjId(id=’some_label_id’, type=’<type>’)

  • Attribute or Metric depending on the type of filter

Defaults to None.

Returns:

pandas.Series: The resulting pandas Series instance.