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# pandas.api.typing.Window.var

Window.var(ddof=1, numeric_only=False, **kwargs)

  Calculate the rolling weighted window variance.

  The window type is determined by the `win_type` parameter specified in `DataFrame.rolling()` or `Series.rolling()`. Additional keyword arguments are passed to the SciPy window function.

  Parameters:
    ddof int, default 1

      Delta Degrees of Freedom. The divisor used in calculations is `N - ddof`, where `N` represents the number of elements.

    numeric_only bool, default False

      Include only float, int, boolean columns.

    **kwargs

      Keyword arguments to configure the `SciPy` weighted window type.

  Returns:
    Series or DataFrame

      Return type is the same as the original object with `np.float64` dtype.

  See also

  `Series.rolling`

    Calling rolling with Series data.

  `DataFrame.rolling`

    Calling rolling with DataFrames.

  `Series.var`

    Aggregating var for Series.

  `DataFrame.var`

    Aggregating var for DataFrame.

  Examples

      >>> ser = pd.Series([0, 1, 5, 2, 8])

  To get an instance of `Window` we need to pass the parameter win_type.

      >>> type(ser.rolling(2, win_type="gaussian"))
      <class 'pandas.api.typing.Window'>

  In order to use the SciPy Gaussian window we need to provide the parameters M and std. The parameter M corresponds to 2 in our example. We pass the second parameter std as a parameter of the following method:

      >>> ser.rolling(2, win_type="gaussian").var(std=3)
      0     NaN
      1     0.5
      2     8.0
      3     4.5
      4    18.0
      dtype: float64