turicreate.SArray.rolling_mean — Turi Create API 6.4.1 documentation (original) (raw)
Calculate a new SArray of the mean of different subsets over this SArray.
Also known as a “moving average” or “running average”. The subset that the mean is calculated over is defined as an inclusive range relative to the position to each value in the SArray, using window_start andwindow_end. For a better understanding of this, see the examples below.
| Parameters: | window_start : int The start of the subset to calculate the mean relative to the current value. window_end : int The end of the subset to calculate the mean relative to the current value. Must be greater than window_start. min_observations : int Minimum number of non-missing observations in window required to calculate the mean (otherwise result is None). None signifies that the entire window must not include a missing value. A negative number throws an error. |
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| Returns: | out : SArray |
Examples
import pandas sa = SArray([1,2,3,4,5]) series = pandas.Series([1,2,3,4,5])
A rolling mean with a window including the previous 2 entries including the current: >>> sa.rolling_mean(-2,0) dtype: float Rows: 5 [None, None, 2.0, 3.0, 4.0]
Pandas equivalent: >>> pandas.rolling_mean(series, 3) 0 NaN 1 NaN 2 2 3 3 4 4 dtype: float64
Same rolling mean operation, but 2 minimum observations: >>> sa.rolling_mean(-2,0,min_observations=2) dtype: float Rows: 5 [None, 1.5, 2.0, 3.0, 4.0]
Pandas equivalent: >>> pandas.rolling_mean(series, 3, min_periods=2) 0 NaN 1 1.5 2 2.0 3 3.0 4 4.0 dtype: float64
A rolling mean with a size of 3, centered around the current: >>> sa.rolling_mean(-1,1) dtype: float Rows: 5 [None, 2.0, 3.0, 4.0, None]
Pandas equivalent: >>> pandas.rolling_mean(series, 3, center=True) 0 NaN 1 2 2 3 3 4 4 NaN dtype: float64
A rolling mean with a window including the current and the 2 entries following: >>> sa.rolling_mean(0,2) dtype: float Rows: 5 [2.0, 3.0, 4.0, None, None]
A rolling mean with a window including the previous 2 entries NOT including the current: >>> sa.rolling_mean(-2,-1) dtype: float Rows: 5 [None, None, 1.5, 2.5, 3.5]