# xarray.core.groupby.DataArrayGroupBy

class `xarray.core.groupby.``DataArrayGroupBy`(obj, group, squeeze=False, grouper=None, bins=None, cut_kwargs={})

  GroupBy object specialized to grouping DataArray objects

  `__init__`(obj, group, squeeze=False, grouper=None, bins=None, cut_kwargs={})

    Create a GroupBy object

    Parameters:

    obj : Dataset or DataArray

    > Object to group.

    group : DataArray

    > Array with the group values.

    squeeze : boolean, optional

    > If “group” is a coordinate of object, squeeze controls whether the subarrays have a dimension of length 1 along that coordinate or if the dimension is squeezed out.

    grouper : pd.Grouper, optional

    > Used for grouping values along the group array.

    bins : array-like, optional

    > If bins is specified, the groups will be discretized into the specified bins by pandas.cut.

    cut_kwargs : dict, optional

    > Extra keyword arguments to pass to pandas.cut

  Methods

  `__init__`(obj, group[, squeeze, grouper, …]) | Create a GroupBy object
  `all`([dim, axis, keep_attrs]) | Reduce this DataArrayGroupBy’s data by applying all along some dimension(s).
  `any`([dim, axis, keep_attrs]) | Reduce this DataArrayGroupBy’s data by applying any along some dimension(s).
  `apply`(func[, shortcut]) | Apply a function over each array in the group and concatenate them together into a new array.
  `argmax`([dim, axis, skipna, keep_attrs]) | Reduce this DataArrayGroupBy’s data by applying argmax along some dimension(s).
  `argmin`([dim, axis, skipna, keep_attrs]) | Reduce this DataArrayGroupBy’s data by applying argmin along some dimension(s).
  `assign_coords`(**kwargs) | Assign coordinates by group.
  `count`([dim, axis, keep_attrs]) | Reduce this DataArrayGroupBy’s data by applying count along some dimension(s).
  `fillna`(value) | Fill missing values in this object by group.
  `first`([skipna, keep_attrs]) | Return the first element of each group along the group dimension
  `last`([skipna, keep_attrs]) | Return the last element of each group along the group dimension
  `max`([dim, axis, skipna, keep_attrs]) | Reduce this DataArrayGroupBy’s data by applying max along some dimension(s).
  `mean`([dim, axis, skipna, keep_attrs]) | Reduce this DataArrayGroupBy’s data by applying mean along some dimension(s).
  `median`([dim, axis, skipna, keep_attrs]) | Reduce this DataArrayGroupBy’s data by applying median along some dimension(s).
  `min`([dim, axis, skipna, keep_attrs]) | Reduce this DataArrayGroupBy’s data by applying min along some dimension(s).
  `prod`([dim, axis, skipna, keep_attrs]) | Reduce this DataArrayGroupBy’s data by applying prod along some dimension(s).
  `reduce`(func[, dim, axis, keep_attrs, shortcut]) | Reduce the items in this group by applying func along some dimension(s).
  `std`([dim, axis, skipna, keep_attrs]) | Reduce this DataArrayGroupBy’s data by applying std along some dimension(s).
  `sum`([dim, axis, skipna, keep_attrs]) | Reduce this DataArrayGroupBy’s data by applying sum along some dimension(s).
  `var`([dim, axis, skipna, keep_attrs]) | Reduce this DataArrayGroupBy’s data by applying var along some dimension(s).
  `where`(cond[, other]) | Return elements from self or other depending on cond.