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]
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
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
stack_on
stack_on_exclude
stack_on_self
uses_calc_time
uses_full_dataframe