def apply(self, func, keep_attrs=None, args=(), **kwargs):
        """Apply a function over the data variables in this dataset.

        Parameters
        ----------
        func : function
            Function which can be called in the form `func(x, *args, **kwargs)`
            to transform each DataArray `x` in this dataset into another
            DataArray.
        keep_attrs : bool, optional
            If True, the dataset's attributes (`attrs`) will be copied from
            the original object to the new one. If False, the new object will
            be returned without attributes.
        args : tuple, optional
            Positional arguments passed on to `func`.
        **kwargs : dict
            Keyword arguments passed on to `func`.

        Returns
        -------
        applied : Dataset
            Resulting dataset from applying ``func`` over each data variable.

        Examples
        --------
        >>> da = xr.DataArray(np.random.randn(2, 3))
        >>> ds = xr.Dataset({'foo': da, 'bar': ('x', [-1, 2])})
        >>> ds
        <xarray.Dataset>
        Dimensions:  (dim_0: 2, dim_1: 3, x: 2)
        Dimensions without coordinates: dim_0, dim_1, x
        Data variables:
            foo      (dim_0, dim_1) float64 -0.3751 -1.951 -1.945 0.2948 0.711 -0.3948
            bar      (x) int64 -1 2
        >>> ds.apply(np.fabs)
        <xarray.Dataset>
        Dimensions:  (dim_0: 2, dim_1: 3, x: 2)
        Dimensions without coordinates: dim_0, dim_1, x
        Data variables:
            foo      (dim_0, dim_1) float64 0.3751 1.951 1.945 0.2948 0.711 0.3948
            bar      (x) float64 1.0 2.0
        """  # noqa
        variables = OrderedDict(
            (k, maybe_wrap_array(v, func(v, *args, **kwargs)))
            for k, v in self.data_vars.items())
        if keep_attrs is None:
            keep_attrs = _get_keep_attrs(default=False)
        attrs = self.attrs if keep_attrs else None
        return type(self)(variables, attrs=attrs)
def apply ( self , func , keep_attrs = None , args = ( ) , ** kwargs ) : variables = OrderedDict ( ( k , maybe_wrap_array ( v , func ( v , * args , ** kwargs ) ) ) for k , v in self . data_vars . items ( ) ) if keep_attrs is None : keep_attrs = _get_keep_attrs ( default = False ) attrs = self . attrs if keep_attrs else None return type ( self ) ( variables , attrs = attrs )
Apply a function over the data variables in this dataset.

        Parameters
        ----------
        func : function
            Function which can be called in the form `func(x, *args, **kwargs)`
            to transform each DataArray `x` in this dataset into another
            DataArray.
        keep_attrs : bool, optional
            If True, the dataset's attributes (`attrs`) will be copied from
            the original object to the new one. If False, the new object will
            be returned without attributes.
        args : tuple, optional
            Positional arguments passed on to `func`.
        **kwargs : dict
            Keyword arguments passed on to `func`.

        Returns
        -------
        applied : Dataset
            Resulting dataset from applying ``func`` over each data variable.

        Examples
        --------
        >>> da = xr.DataArray(np.random.randn(2, 3))
        >>> ds = xr.Dataset({'foo': da, 'bar': ('x', [-1, 2])})
        >>> ds
        <xarray.Dataset>
        Dimensions:  (dim_0: 2, dim_1: 3, x: 2)
        Dimensions without coordinates: dim_0, dim_1, x
        Data variables:
            foo      (dim_0, dim_1) float64 -0.3751 -1.951 -1.945 0.2948 0.711 -0.3948
            bar      (x) int64 -1 2
        >>> ds.apply(np.fabs)
        <xarray.Dataset>
        Dimensions:  (dim_0: 2, dim_1: 3, x: 2)
        Dimensions without coordinates: dim_0, dim_1, x
        Data variables:
            foo      (dim_0, dim_1) float64 0.3751 1.951 1.945 0.2948 0.711 0.3948
            bar      (x) float64 1.0 2.0