featuretools.primitives.RollingSTD¶
- class featuretools.primitives.RollingSTD(window_length=3, gap=0, min_periods=1)[source]¶
Calculates the standard deviation of entries over a given window.
- Description:
Given a list of numbers and a corresponding list of datetimes, return a rolling standard deviation of the numeric values, 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_std = RollingSTD(window_length=4) >>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5) >>> rolling_std(times, [4, 3, 2, 1, 0]).tolist() [nan, 0.7071067811865476, 1.0, 1.2909944487358056, 1.2909944487358056]
We can also control the gap before the rolling calculation.
>>> import pandas as pd >>> rolling_std = RollingSTD(window_length=4, gap=1) >>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5) >>> rolling_std(times, [4, 3, 2, 1, 0]).tolist() [nan, nan, 0.7071067811865476, 1.0, 1.2909944487358056]
We can also control the minimum number of periods required for the rolling calculation.
>>> import pandas as pd >>> rolling_std = RollingSTD(window_length=4, min_periods=4) >>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5) >>> rolling_std(times, [4, 3, 2, 1, 0]).tolist() [nan, nan, nan, 1.2909944487358056, 1.2909944487358056]
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