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.
load_weather([nrows, return_single_table])	Load the Australian daily-min-temperatures weather dataset.

Deep Feature Synthesis

dfs([dataframes, relationships, entityset, ...])	Calculates a feature matrix and features given a dictionary of dataframes and a list of relationships.
get_valid_primitives(entityset, ...[, ...])	Returns two lists of primitives (transform and aggregation) containing primitives that can be applied to the specific target dataframe 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 dataframe that is a based off one or more other features in that dataframe.
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 column over time.
Entropy([dropna, base])	Calculates the entropy for a categorical column

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.

Rolling Transform Primitives

RollingCount([window_length, gap, min_periods])	Determines a rolling count of events over a given window.
RollingMax([window_length, gap, min_periods])	Determines the maximum of entries over a given window.
RollingMean([window_length, gap, min_periods])	Calculates the mean of entries over a given window.
RollingMin([window_length, gap, min_periods])	Determines the minimum of entries over a given window.
RollingSTD([window_length, gap, min_periods])	Calculates the standard deviation of entries over a given window.

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

CityblockDistance([unit])	Calculates the distance between points in a city road grid.
GeoMidpoint()	Determines the geographic center of two coordinates.
Haversine([unit])	Calculates the approximate haversine distance between two LatLong columns.
IsInGeoBox([point1, point2])	Determines if coordinates are inside a box defined by two corner coordinate points.
Latitude()	Returns the first tuple value in a list of LatLong tuples.
Longitude()	Returns the second tuple value in a list of LatLong tuples.

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.

Natural Language Processing Primitives

Natural Language Processing primitives create features for textual data. For more information on how to use and install these primitives, see here.

DiversityScore()	Calculates the overall complexity of the text based on the total
LSA()	Calculates the Latent Semantic Analysis Values of NaturalLanguage Input
MeanCharactersPerWord()	Determines the mean number of characters per word.
PartOfSpeechCount()	Calculates the occurences of each different part of speech.
PolarityScore()	Calculates the polarity of a text on a scale from -1 (negative) to 1 (positive)
PunctuationCount()	Determines number of punctuation characters in a string.
StopwordCount()	Determines number of stopwords in a string.
TitleWordCount()	Determines the number of title words in a string.
UniversalSentenceEncoder()	Transforms a sentence or short paragraph to a vector using [tfhub model](https://tfhub.dev/google/universal-sentence-encoder/2)
UpperCaseCount()	Calculates the number of upper case letters in text.

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, Relationship

Constructors

EntitySet([id, dataframes, relationships])	Stores all actual data and typing information for an entityset
Relationship(entityset, ...)	Class to represent a relationship between dataframes

EntitySet load and prepare data

EntitySet.add_dataframe(dataframe[, ...])	Add a DataFrame to the EntitySet with Woodwork typing information.
EntitySet.add_interesting_values([...])	Find or set interesting values for categorical columns, to be used to generate "where" clauses
EntitySet.add_last_time_indexes([...])	Calculates the last time index values for each dataframe (the last time an instance or children of that instance were observed).
EntitySet.add_relationship([...])	Add a new relationship between dataframes in the entityset.
EntitySet.add_relationships(relationships)	Add multiple new relationships to a entityset
EntitySet.concat(other[, inplace])	Combine entityset with another to create a new entityset with the combined data of both entitysets.
EntitySet.normalize_dataframe(...[, ...])	Create a new dataframe and relationship from unique values of an existing column.
EntitySet.set_secondary_time_index(...)	Set the secondary time index for a dataframe in the EntitySet using its dataframe name.
EntitySet.replace_dataframe(dataframe_name, df)	Replace the internal dataframe of an EntitySet table, keeping Woodwork typing information the same.

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__(dataframe_name)	Get dataframe instance from entityset
EntitySet.find_backward_paths(...)	Generator which yields all backward paths between a start and goal dataframe.
EntitySet.find_forward_paths(...)	Generator which yields all forward paths between a start and goal dataframe.
EntitySet.get_forward_dataframes(dataframe_name)	Get dataframes that are in a forward relationship with dataframe
EntitySet.get_backward_dataframes(dataframe_name)	Get dataframes that are in a backward relationship with dataframe
EntitySet.query_by_values(dataframe_name, ...)	Query instances that have column with given value

EntitySet visualization

EntitySet.plot([to_file])

Create a UML diagram-ish graph of the EntitySet.

Relationship attributes

Relationship.parent_column	Column in parent dataframe
Relationship.child_column	Column in child dataframe
Relationship.parent_dataframe	Parent dataframe object
Relationship.child_dataframe	Child dataframe object

Data Type Util Methods

list_logical_types()	Returns a dataframe describing all of the available Logical Types.
list_semantic_tags()	Returns a dataframe describing all of the common semantic tags.

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