@doc(template_header, create_section_header('Parameters'), dedent('\n        bias : bool, default False\n            Use a standard estimation bias correction.\n        ').replace('\n', '', 1), args_compat, kwargs_compat, create_section_header('Returns'), template_returns, create_section_header('See Also'), template_see_also[:(- 1)], window_method='ewm', aggregation_description='(exponential weighted moment) variance', agg_method='var')
def var(self, bias: bool=False, *args, **kwargs):
    nv.validate_window_func('var', args, kwargs)
    window_func = window_aggregations.ewmcov
    wfunc = partial(window_func, com=self._com, adjust=self.adjust, ignore_na=self.ignore_na, bias=bias)

    def var_func(values, begin, end, min_periods):
        return wfunc(values, begin, end, min_periods, values)
    return self._apply(var_func)
@doc(template_header, create_section_header('Parameters'), dedent('\n        bias : bool, default False\n            Use a standard estimation bias correction.\n        ').replace('\n', , 1), args_compat, kwargs_compat, create_section_header('Returns'), template_returns, create_section_header('See Also'), template_see_also[:(- 1)], window_method='ewm', aggregation_description='(exponential weighted moment) variance', agg_method='var')
def var(self, bias: bool=False, *args, **kwargs):
    nv.validate_window_func('var', args, kwargs)
    window_func = window_aggregations.ewmcov
    wfunc = partial(window_func, com=self._com, adjust=self.adjust, ignore_na=self.ignore_na, bias=bias)

    def var_func(values, begin, end, min_periods):
        return wfunc(values, begin, end, min_periods, values)
    return self._apply(var_func)