docs: 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})