def groupby_bins(self, group, bins, right: bool = True, labels=None,
                     precision: int = 3, include_lowest: bool = False,
                     squeeze: bool = True):
        """Returns a GroupBy object for performing grouped operations.

        Rather than using all unique values of `group`, the values are discretized
        first by applying `pandas.cut` [1]_ to `group`.

        Parameters
        ----------
        group : str, DataArray or IndexVariable
            Array whose binned values should be used to group this array. If a
            string, must be the name of a variable contained in this dataset.
        bins : int or array of scalars
            If bins is an int, it defines the number of equal-width bins in the
            range of x. However, in this case, the range of x is extended by .1%
            on each side to include the min or max values of x. If bins is a
            sequence it defines the bin edges allowing for non-uniform bin
            width. No extension of the range of x is done in this case.
        right : boolean, optional
            Indicates whether the bins include the rightmost edge or not. If
            right == True (the default), then the bins [1,2,3,4] indicate
            (1,2], (2,3], (3,4].
        labels : array or boolean, default None
            Used as labels for the resulting bins. Must be of the same length as
            the resulting bins. If False, string bin labels are assigned by
            `pandas.cut`.
        precision : int
            The precision at which to store and display the bins labels.
        include_lowest : bool
            Whether the first interval should be left-inclusive or not.
        squeeze : boolean, optional
            If "group" is a dimension of any arrays in this dataset, `squeeze`
            controls whether the subarrays have a dimension of length 1 along
            that dimension or if the dimension is squeezed out.

        Returns
        -------
        grouped : GroupBy
            A `GroupBy` object patterned after `pandas.GroupBy` that can be
            iterated over in the form of `(unique_value, grouped_array)` pairs.
            The name of the group has the added suffix `_bins` in order to
            distinguish it from the original variable.

        References
        ----------
        .. [1] http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html
        """  # noqa
        return self._groupby_cls(self, group, squeeze=squeeze, bins=bins,
                                 cut_kwargs={'right': right, 'labels': labels,
                                             'precision': precision,
                                             'include_lowest': include_lowest})
Returns a GroupBy object for performing grouped operations.

        Rather than using all unique values of `group`, the values are discretized
        first by applying `pandas.cut` [1]_ to `group`.

        Parameters
        ----------
        group : str, DataArray or IndexVariable
            Array whose binned values should be used to group this array. If a
            string, must be the name of a variable contained in this dataset.
        bins : int or array of scalars
            If bins is an int, it defines the number of equal-width bins in the
            range of x. However, in this case, the range of x is extended by .1%
            on each side to include the min or max values of x. If bins is a
            sequence it defines the bin edges allowing for non-uniform bin
            width. No extension of the range of x is done in this case.
        right : boolean, optional
            Indicates whether the bins include the rightmost edge or not. If
            right == True (the default), then the bins [1,2,3,4] indicate
            (1,2], (2,3], (3,4].
        labels : array or boolean, default None
            Used as labels for the resulting bins. Must be of the same length as
            the resulting bins. If False, string bin labels are assigned by
            `pandas.cut`.
        precision : int
            The precision at which to store and display the bins labels.
        include_lowest : bool
            Whether the first interval should be left-inclusive or not.
        squeeze : boolean, optional
            If "group" is a dimension of any arrays in this dataset, `squeeze`
            controls whether the subarrays have a dimension of length 1 along
            that dimension or if the dimension is squeezed out.

        Returns
        -------
        grouped : GroupBy
            A `GroupBy` object patterned after `pandas.GroupBy` that can be
            iterated over in the form of `(unique_value, grouped_array)` pairs.
            The name of the group has the added suffix `_bins` in order to
            distinguish it from the original variable.

        References
        ----------
        .. [1] http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html