featuretools.primitives.ExpandingMean

class featuretools.primitives.ExpandingMean(gap=1, min_periods=1)

Computes the expanding mean of events over a given window.

Description:

Given a list of datetimes, returns an expanding mean starting at the row gap rows away from the current row. An expanding primitive calculates the value of a primitive for a given time with all the data available up to the corresponding point in time.

Input datetimes should be monotonic.

Parameters:

• gap (int, optional) – Specifies a gap backwards from each instance before the usable data begins. Corresponds to number of rows. Defaults to 1.

• min_periods (int, optional) – Minimum number of observations required for performing calculations over the window. Defaults to 1.

Examples

>>> import pandas as pd
>>> expanding_mean = ExpandingMean()
>>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
>>> expanding_mean(times, [5, 4, 3, 2, 1]).tolist()
[nan, 5.0, 4.5, 4.0, 3.5]

We can also control the gap before the expanding calculation.

>>> import pandas as pd
>>> expanding_mean = ExpandingMean(gap=0)
>>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
>>> expanding_mean(times, [5, 4, 3, 2, 1]).tolist()
[5.0, 4.5, 4.0, 3.5, 3.0]

We can also control the minimum number of periods required for the rolling calculation.

>>> import pandas as pd
>>> expanding_mean = ExpandingMean(min_periods=3)
>>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
>>> expanding_mean(times, [5, 4, 3, 2, 1]).tolist()
[nan, nan, nan, 4.0, 3.5]

__init__(gap=1, min_periods=1)

Methods

__init__([gap, min_periods])
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