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).