featuretools.EntitySet

class featuretools.EntitySet(id=None, entities=None, relationships=None)

Stores all actual data for a entityset

id

entity_dict

relationships

time_type

Properties:

metadata

__init__(id=None, entities=None, relationships=None)

Creates EntitySet

Parameters

• id (str) – Unique identifier to associate with this instance

• entities (dict[str -> tuple(pd.DataFrame, str, str, dict[str -> Variable])]) – dictionary of entities. Entries take the format {entity id -> (dataframe, id column, (time_index), (variable_types), (make_index))}. Note that time_index, variable_types and make_index are optional.

• relationships (list[(str, str, str, str)]) – List of relationships between entities. List items are a tuple with the format (parent entity id, parent variable, child entity id, child variable).

Example

entities = {
    "cards" : (card_df, "id"),
    "transactions" : (transactions_df, "id", "transaction_time")
}

relationships = [("cards", "id", "transactions", "card_id")]

ft.EntitySet("my-entity-set", entities, relationships)

Methods

__init__([id, entities, relationships])	Creates EntitySet
add_interesting_values([max_values, verbose])	Find interesting values for categorical variables, to be used to generate “where” clauses
add_last_time_indexes([updated_entities])	Calculates the last time index values for each entity (the last time an instance or children of that instance were observed).
add_relationship(relationship)	Add a new relationship between entities in the entityset
add_relationships(relationships)	Add multiple new relationships to a entityset
concat(other[, inplace])	Combine entityset with another to create a new entityset with the combined data of both entitysets.
entity_from_dataframe(entity_id, dataframe)	Load the data for a specified entity from a Pandas DataFrame.
find_backward_paths(start_entity_id, …)	Generator which yields all backward paths between a start and goal entity.
find_forward_paths(start_entity_id, …)	Generator which yields all forward paths between a start and goal entity.
get_backward_entities(entity_id[, deep])	Get entities that are in a backward relationship with entity
get_backward_relationships(entity_id)	get relationships where entity “entity_id” is the parent.
get_forward_entities(entity_id[, deep])	Get entities that are in a forward relationship with entity
get_forward_relationships(entity_id)	Get relationships where entity “entity_id” is the child
has_unique_forward_path(start_entity_id, …)	Is the forward path from start to end unique?
normalize_entity(base_entity_id, …[, …])	Create a new entity and relationship from unique values of an existing variable.
plot([to_file])	Create a UML diagram-ish graph of the EntitySet.
reset_data_description()
to_csv(path[, sep, encoding, engine, …])	Write entityset to disk in the csv format, location specified by path.
to_dictionary()
to_parquet(path[, engine, compression, …])	Write entityset to disk in the parquet format, location specified by path.
to_pickle(path[, compression, profile_name])	Write entityset in the pickle format, location specified by path.

Attributes

entities
metadata	Returns the metadata for this EntitySet.