# xarray.Dataset.groupby_bins

`Dataset.``groupby_bins`(group, bins, right=True, labels=None, precision=3, include_lowest=False, squeeze=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 [R6565] 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

  [R6565] | (1, 2) http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html