featuretools.primitives.TimeSincePrevious#

class featuretools.primitives.TimeSincePrevious(unit='seconds')[source]#

Computes the time since the previous entry in a list.

Parameters:

unit (str) – Defines the unit of time to count from. Defaults to Seconds. Acceptable values: years, months, days, hours, minutes, seconds, milliseconds, nanoseconds

Description:

Given a list of datetimes, compute the time in seconds elapsed since the previous item in the list. The result for the first item in the list will always be NaN.

Examples

>>> from datetime import datetime
>>> time_since_previous = TimeSincePrevious()
>>> dates = [datetime(2019, 3, 1, 0, 0, 0),
...          datetime(2019, 3, 1, 0, 2, 0),
...          datetime(2019, 3, 1, 0, 3, 0),
...          datetime(2019, 3, 1, 0, 2, 30),
...          datetime(2019, 3, 1, 0, 10, 0)]
>>> time_since_previous(dates).tolist()
[nan, 120.0, 60.0, -30.0, 450.0]
__init__(unit='seconds')[source]#

Methods

__init__([unit])

flatten_nested_input_types(input_types)

Flattens nested column schema inputs into a single list.

generate_name(base_feature_names)

generate_names(base_feature_names)

get_args_string()

get_arguments()

get_description(input_column_descriptions[, ...])

get_filepath(filename)

get_function()

Attributes

base_of

base_of_exclude

commutative

compatibility

Additional compatible libraries

default_value

Default value this feature returns if no data found.

description_template

input_types

woodwork.ColumnSchema types of inputs

max_stack_depth

name

Name of the primitive

number_output_features

Number of columns in feature matrix associated with this feature

return_type

ColumnSchema type of return

series_library

stack_on

stack_on_exclude

stack_on_self

uses_calc_time

uses_full_dataframe