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# 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).

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