featuretools.primitives.TimeSincePrevious

class featuretools.primitives.TimeSincePrevious(unit='seconds')

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')

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