pandas.rolling_window

pandas.rolling_window(arg, window=None, win_type=None, min_periods=None, freq=None, center=False, mean=True, axis=0, how=None, **kwargs)

Applies a moving window of type window_type and size window on the data.

Parameters:

arg : Series, DataFrame

window : int or ndarray

Weighting window specification. If the window is an integer, then it is treated as the window length and win_type is required

win_type : str, default None

Window type (see Notes)

min_periods : int, default None

Minimum number of observations in window required to have a value (otherwise result is NA).

freq : string or DateOffset object, optional (default None)

Frequency to conform the data to before computing the statistic. Specified as a frequency string or DateOffset object.

center : boolean, default False

Whether the label should correspond with center of window

mean : boolean, default True

If True computes weighted mean, else weighted sum

axis : {0, 1}, default 0

how : string, default ‘mean’

Method for down- or re-sampling

Returns:

y : type of input argument

Notes

The recognized window types are:

• boxcar
• triang
• blackman
• hamming
• bartlett
• parzen
• bohman
• blackmanharris
• nuttall
• barthann
• kaiser (needs beta)
• gaussian (needs std)
• general_gaussian (needs power, width)
• slepian (needs width).

By default, the result is set to the right edge of the window. This can be changed to the center of the window by setting center=True.

The freq keyword is used to conform time series data to a specified frequency by resampling the data. This is done with the default parameters of resample() (i.e. using the mean).