def reduce(self, dimensions=[], function=None, spreadfn=None, **reductions):
        """Applies reduction along the specified dimension(s).

        Allows reducing the values along one or more key dimension
        with the supplied function. Supports two signatures:

        Reducing with a list of dimensions, e.g.:

            ds.reduce(['x'], np.mean)

        Defining a reduction using keywords, e.g.:

            ds.reduce(x=np.mean)

        Args:
            dimensions: Dimension(s) to apply reduction on
                Defaults to all key dimensions
            function: Reduction operation to apply, e.g. numpy.mean
            spreadfn: Secondary reduction to compute value spread
                Useful for computing a confidence interval, spread, or
                standard deviation.
            **reductions: Keyword argument defining reduction
                Allows reduction to be defined as keyword pair of
                dimension and function

        Returns:
            The Dataset after reductions have been applied.
        """
        if any(dim in self.vdims for dim in dimensions):
            raise Exception("Reduce cannot be applied to value dimensions")
        function, dims = self._reduce_map(dimensions, function, reductions)
        dims = [d for d in self.kdims if d not in dims]
        return self.aggregate(dims, function, spreadfn)
def reduce ( self , dimensions = [ ] , function = None , spreadfn = None , ** reductions ) : if any ( dim in self . vdims for dim in dimensions ) : raise Exception ( "Reduce cannot be applied to value dimensions" ) function , dims = self . _reduce_map ( dimensions , function , reductions ) dims = [ d for d in self . kdims if d not in dims ] return self . aggregate ( dims , function , spreadfn )
Applies reduction along the specified dimension(s).

        Allows reducing the values along one or more key dimension
        with the supplied function. Supports two signatures:

        Reducing with a list of dimensions, e.g.:

            ds.reduce(['x'], np.mean)

        Defining a reduction using keywords, e.g.:

            ds.reduce(x=np.mean)

        Args:
            dimensions: Dimension(s) to apply reduction on
                Defaults to all key dimensions
            function: Reduction operation to apply, e.g. numpy.mean
            spreadfn: Secondary reduction to compute value spread
                Useful for computing a confidence interval, spread, or
                standard deviation.
            **reductions: Keyword argument defining reduction
                Allows reduction to be defined as keyword pair of
                dimension and function

        Returns:
            The Dataset after reductions have been applied.