NOTICE

The upcoming release of Featuretools 1.0.0 contains several breaking changes. Users are encouraged to test this version prior to release by installing from GitHub:

pip install https://github.com/alteryx/featuretools/archive/woodwork-integration.zip

For details on migrating to the new version, refer to Transitioning to Featuretools Version 1.0. Please report any issues in the Featuretools GitHub repo or by messaging in Alteryx Open Source Slack.

API Reference

Demo Datasets

load_retail([id, nrows, return_single_table])	Returns the retail entityset example.
load_mock_customer([n_customers, …])	Return dataframes of mock customer data
load_flight([month_filter, …])	Download, clean, and filter flight data from 2017.

Deep Feature Synthesis

dfs([entities, relationships, entityset, …])	Calculates a feature matrix and features given a dictionary of entities and a list of relationships.
get_valid_primitives(entityset, target_entity)	Returns two lists of primitives (transform and aggregation) containing primitives that can be applied to the specific target entity to create features.

Timedelta

Timedelta(value[, unit, delta_obj])

Represents differences in time.

Time utils

make_temporal_cutoffs(instance_ids, cutoffs)

Makes a set of equally spaced cutoff times prior to a set of input cutoffs and instance ids.

Feature Primitives

A list of all Featuretools primitives can be obtained by visiting primitives.featurelabs.com.

Primitive Types

TransformPrimitive()	Feature for entity that is a based off one or more other features in that entity.
AggregationPrimitive()

Primitive Creation Functions

make_agg_primitive(function, input_types, …)	Returns a new aggregation primitive class.
make_trans_primitive(function, input_types, …)	Returns a new transform primitive class

Aggregation Primitives

Count()	Determines the total number of values, excluding NaN.
Mean([skipna])	Computes the average for a list of values.
Sum()	Calculates the total addition, ignoring NaN.
Min()	Calculates the smallest value, ignoring NaN values.
Max()	Calculates the highest value, ignoring NaN values.
Std()	Computes the dispersion relative to the mean value, ignoring NaN.
Median()	Determines the middlemost number in a list of values.
Mode()	Determines the most commonly repeated value.
AvgTimeBetween([unit])	Computes the average number of seconds between consecutive events.
TimeSinceLast([unit])	Calculates the time elapsed since the last datetime (default in seconds).
TimeSinceFirst([unit])	Calculates the time elapsed since the first datetime (in seconds).
NumUnique()	Determines the number of distinct values, ignoring NaN values.
PercentTrue()	Determines the percent of True values.
All()	Calculates if all values are ‘True’ in a list.
Any()	Determines if any value is ‘True’ in a list.
First()	Determines the first value in a list.
Last()	Determines the last value in a list.
Skew()	Computes the extent to which a distribution differs from a normal distribution.
Trend()	Calculates the trend of a variable over time.
Entropy([dropna, base])	Calculates the entropy for a categorical variable

Transform Primitives

Combine features

IsIn([list_of_outputs])	Determines whether a value is present in a provided list.
And()	Element-wise logical AND of two lists.
Or()	Element-wise logical OR of two lists.
Not()	Negates a boolean value.

General Transform Primitives

Absolute()	Computes the absolute value of a number.
Percentile()	Determines the percentile rank for each value in a list.
TimeSince([unit])	Calculates time from a value to a specified cutoff datetime.

Datetime Transform Primitives

Second()	Determines the seconds value of a datetime.
Minute()	Determines the minutes value of a datetime.
Weekday()	Determines the day of the week from a datetime.
IsWeekend()	Determines if a date falls on a weekend.
Hour()	Determines the hour value of a datetime.
Day()	Determines the day of the month from a datetime.
Week()	Determines the week of the year from a datetime.
Month()	Determines the month value of a datetime.
Year()	Determines the year value of a datetime.

Cumulative Transform Primitives

Diff()	Compute the difference between the value in a list and the previous value in that list.
TimeSincePrevious([unit])	Compute the time since the previous entry in a list.
CumCount()	Calculates the cumulative count.
CumSum()	Calculates the cumulative sum.
CumMean()	Calculates the cumulative mean.
CumMin()	Calculates the cumulative minimum.
CumMax()	Calculates the cumulative maximum.

NaturalLanguage Transform Primitives

NumCharacters()	Calculates the number of characters in a string.
NumWords()	Determines the number of words in a string by counting the spaces.

Location Transform Primitives

Latitude()	Returns the first tuple value in a list of LatLong tuples.
Longitude()	Returns the second tuple value in a list of LatLong tuples.
Haversine([unit])	Calculates the approximate haversine distance between two LatLong variable types.

Feature methods

FeatureBase.rename(name)	Rename Feature, returns copy
FeatureBase.get_depth([stop_at])	Returns depth of feature

Feature calculation

calculate_feature_matrix(features[, …])

Calculates a matrix for a given set of instance ids and calculation times.

Feature descriptions

describe_feature(feature[, …])

Generates an English language description of a feature.

Feature visualization

graph_feature(feature[, to_file, description])

Generates a feature lineage graph for the given feature

Feature encoding

encode_features(feature_matrix, features[, …])

Encode categorical features

Feature Selection

remove_low_information_features(feature_matrix)	Select features that have at least 2 unique values and that are not all null
remove_highly_correlated_features(feature_matrix)	Removes columns in feature matrix that are highly correlated with another column.
remove_highly_null_features(feature_matrix)	Removes columns from a feature matrix that have higher than a set threshold of null values.
remove_single_value_features(feature_matrix)	Removes columns in feature matrix where all the values are the same.

Feature Matrix utils

replace_inf_values(feature_matrix[, …])

Replace all np.inf values in a feature matrix with the specified replacement value.

Saving and Loading Features

save_features(features[, location, profile_name])	Saves the features list as JSON to a specified filepath/S3 path, writes to an open file, or returns the serialized features as a JSON string.
load_features(features[, profile_name])	Loads the features from a filepath, S3 path, URL, an open file, or a JSON formatted string.

EntitySet, Entity, Relationship, Variable Types

Constructors

EntitySet([id, entities, relationships])	Stores all actual data for a entityset
Entity(id, df, entityset[, variable_types, …])	Represents an entity in a Entityset, and stores relevant metadata and data
Relationship(parent_variable, child_variable)	Class to represent an relationship between entities

EntitySet load and prepare data

EntitySet.entity_from_dataframe(entity_id, …)	Load the data for a specified entity from a Pandas DataFrame.
EntitySet.add_relationship(relationship)	Add a new relationship between entities in the entityset
EntitySet.normalize_entity(base_entity_id, …)	Create a new entity and relationship from unique values of an existing variable.
EntitySet.add_interesting_values([…])	Find interesting values for categorical variables, to be used to generate “where” clauses

EntitySet serialization

read_entityset(path[, profile_name])

Read entityset from disk, S3 path, or URL.

EntitySet.to_csv(path[, sep, encoding, …])	Write entityset to disk in the csv format, location specified by path.
EntitySet.to_pickle(path[, compression, …])	Write entityset in the pickle format, location specified by path.
EntitySet.to_parquet(path[, engine, …])	Write entityset to disk in the parquet format, location specified by path.

EntitySet query methods

EntitySet.__getitem__(entity_id)	Get entity instance from entityset
EntitySet.find_backward_paths(…)	Generator which yields all backward paths between a start and goal entity.
EntitySet.find_forward_paths(…)	Generator which yields all forward paths between a start and goal entity.
EntitySet.get_forward_entities(entity_id[, deep])	Get entities that are in a forward relationship with entity
EntitySet.get_backward_entities(entity_id[, …])	Get entities that are in a backward relationship with entity

EntitySet visualization

EntitySet.plot([to_file])

Create a UML diagram-ish graph of the EntitySet.

Entity methods

Entity.convert_variable_type(variable_id, …)	Convert variable in dataframe to different type
Entity.add_interesting_values([max_values, …])	Find interesting values for categorical variables, to be used to

Relationship attributes

Relationship.parent_variable	Instance of variable in parent entity
Relationship.child_variable	Instance of variable in child entity
Relationship.parent_entity	Parent entity object
Relationship.child_entity	Child entity object

Variable types

Index(id, entity[, name, description])	Represents variables that uniquely identify an instance of an entity
Id(id, entity[, name, categories])	Represents variables that identify another entity
TimeIndex(id, entity[, name, description])	Represents time index of entity
DatetimeTimeIndex(id, entity[, name, format])	Represents time index of entity that is a datetime
NumericTimeIndex(id, entity[, name, range, …])	Represents time index of entity that is numeric
Datetime(id, entity[, name, format])	Represents variables that are points in time
Numeric(id, entity[, name, range, …])	Represents variables that contain numeric values
Categorical(id, entity[, name, categories])	Represents variables that can take an unordered discrete values
Ordinal(id, entity[, name])	Represents variables that take on an ordered discrete value
Boolean(id, entity[, name, true_values, …])	Represents variables that take on one of two values
NaturalLanguage(id, entity[, name, description])	Represents variables that are arbitary strings
LatLong(id, entity[, name, description])	Represents an ordered pair (Latitude, Longitude) To make a latlong in a dataframe do data[‘latlong’] = data[[‘latitude’, ‘longitude’]].apply(tuple, axis=1)
ZIPCode(id, entity[, name, categories])	Represents a postal address in the United States.
IPAddress(id, entity[, name, description])	Represents a computer network address.
FullName(id, entity[, name, description])	Represents a person’s full name.
EmailAddress(id, entity[, name, description])	Represents an email box to which email message are sent.
URL(id, entity[, name, description])	Represents a valid web url (with or without http/www)
PhoneNumber(id, entity[, name, description])	Represents any valid phone number.
DateOfBirth(id, entity[, name, format])	Represents a date of birth as a datetime
CountryCode(id, entity[, name, categories])	Represents an ISO-3166 standard country code.
SubRegionCode(id, entity[, name, categories])	Represents an ISO-3166 standard sub-region code.
FilePath(id, entity[, name, description])	Represents a valid filepath, absolute or relative

Variable Utils Methods

find_variable_types()	Retrieves all Variable types as a dictionary where key is type_string
list_variable_types()	Retrieves all Variable types as a dataframe, with the column headers
graph_variable_types([to_file])	Create a UML diagram-ish graph of all the Variables.

Featuretools External Ecosystem featuretools.demo.load_retail