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)]) – Dictionary of entities. Entries take the format {entity id -> (dataframe, id column, (time_column), (variable_types))}. Note that time_column and variable_types 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_path(start_entity_id, …)

Find a backward path between a start and goal entity

find_forward_path(start_entity_id, …)

Find a forward path between a start and goal entity

find_path(start_entity_id, goal_entity_id[, …])

Find a path in the entityset represented as a DAG

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

get_pandas_data_slice(filter_entity_ids, …)

Get the slice of data related to the supplied instances of the index entity.

normalize_entity(base_entity_id, …[, …])

Create a new entity and relationship from unique values of an existing variable.

path_relationships(path, start_entity_id)

Generate a list of the strings “forward” or “backward” corresponding to the direction of the relationship at each point in path.

plot([to_file])

Create a UML diagram-ish graph of the EntitySet.

related_instances(start_entity_id, …[, …])

Filter out all but relevant information from dataframes along path from start_entity_id to final_entity_id, exclude data if it does not lie within and time_last

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])

Write entityset to disk in the pickle format, location specified by path.

Attributes

entities

metadata

Returns the metadata for this EntitySet.