groupby_bins(group, bins, right: bool = True, labels=None, precision: int = 3, include_lowest: bool = False, squeeze: bool = True, restore_coord_dims: Optional[bool] = None)¶
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  to group.
- groupstr, 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.
- binsint 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.
- rightboolean, 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].
- labelsarray 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.
The precision at which to store and display the bins labels.
Whether the first interval should be left-inclusive or not.
- squeezeboolean, 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.
- restore_coord_dimsbool, optional
If True, also restore the dimension order of multi-dimensional coordinates.
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.