featuretools.primitives.ExpandingMax#
- class featuretools.primitives.ExpandingMax(gap=1, min_periods=1)[source]#
Computes the expanding maximum of events over a given window.
- Description:
Given a list of datetimes, returns an expanding maximum 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_min = ExpandingMax() >>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5) >>> expanding_min(times, [2, 4, 6, 7, 2]).tolist() [nan, 2.0, 4.0, 6.0, 7.0]
We can also control the gap before the expanding calculation.
>>> import pandas as pd >>> expanding_min = ExpandingMax(gap=0) >>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5) >>> expanding_min(times, [2, 4, 6, 7, 2]).tolist() [2.0, 4.0, 6.0, 7.0, 7.0]
We can also control the minimum number of periods required for the rolling calculation.
>>> import pandas as pd >>> expanding_min = ExpandingMax(min_periods=3) >>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5) >>> expanding_min(times, [2, 4, 6, 7, 2]).tolist() [nan, nan, nan, 6.0, 7.0]
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