featuretools.primitives.RollingCount

class featuretools.primitives.RollingCount(window_length=3, gap=0, min_periods=0)

Determines a rolling count of events over a given window.

Description:

Given a list of datetimes, return a rolling count starting at the row gap rows away from the current row and looking backward over the specified time window (by window_length and gap).

Input datetimes should be monotonic.

Parameters

• window_length (int) – The number of rows to be included in each frame. For data with a uniform sampling frequency, for example of one day, the window_length will correspond to a period of time, in this case, 7 days for a window_length of 7.

• gap (int, optional) – The number of rows backward from the target instance before the window of usable data begins. Defaults to 0, which will include the target instance in the window.

• min_periods (int, optional) – Minimum number of observations required for a window to have a value. Can only be as large as window_length. Defaults to 1.

Examples

>>> import pandas as pd
>>> rolling_count = RollingCount(window_length=3)
>>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
>>> rolling_count(times).tolist()
[1.0, 2.0, 3.0, 3.0, 3.0]

We can also control the gap before the rolling calculation.

>>> import pandas as pd
>>> rolling_count = RollingCount(window_length=3, gap=1)
>>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
>>> rolling_count(times).tolist()
[nan, 1.0, 2.0, 3.0, 3.0]

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

>>> import pandas as pd
>>> rolling_count = RollingCount(window_length=3, min_periods=3)
>>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
>>> rolling_count(times).tolist()
[nan, nan, 3.0, 3.0, 3.0]

__init__(window_length=3, gap=0, min_periods=0)

Methods

__init__([window_length, gap, min_periods])
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
uses_calc_time
uses_full_dataframe

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