featurebyte.View.create_observation_table

create_observation_table(

name: str,

sample_rows: Optional[int]=None,

columns: Optional[list[str]]=None,

columns_rename_mapping: Optional[dict[str, str]]=None,

context_name: Optional[str]=None,

skip_entity_validation_checks: Optional[bool]=False,

primary_entities: Optional[List[str]]=None

) -> ObservationTable

Description

Creates an ObservationTable from the View. When you specify the columns and the columns_rename_mapping parameters, make sure that the table has:

• a column containing entity values with an accepted serving name.
• a column containing historical points-in-time in UTC. The column name must be "POINT_IN_TIME".

Parameters

• name: str
  Name of the ObservationTable.
  
  
• sample_rows: Optional[int]
  Optionally sample the source table to this number of rows before creating the observation table.
  
  
• columns: Optional[list[str]]
  Include only these columns in the view when creating the observation table. If None, all columns are included.
  
  
• columns_rename_mapping: Optional[dict[str, str]]
  Rename columns in the view using this mapping from old column names to new column names when creating the observation table. If None, no columns are renamed.
  
  
• context_name: Optional[str]
  Context name for the observation table.
  
  
• skip_entity_validation_checks: Optional[bool]
  default: False
  Skip entity validation checks when creating the observation table.
  
  
• primary_entities: Optional[List[str]]
  List of primary entities for the observation table. If None, the primary entities are inferred from the view.
  
  

Returns

• ObservationTable
  ObservationTable object.
  
  

Raises

• ValueError
  If no primary entities are found.
  
  

Examples

>>> observation_table = view.create_observation_table(
...   name="<observation_table_name>",
...   sample_rows=10000,
...   columns=["timestamp", "<entity_serving_name>"],
...   columns_rename_mapping={"timestamp": "POINT_IN_TIME"},
...   context_id=context_id,
... )