Method groupby

xarray/core/dataarray.py:6931–7072

Returns a DataArrayGroupBy object for performing grouped operations. Parameters ---------- group : str or DataArray or IndexVariable or sequence of hashable or mapping of hashable to Grouper Array whose unique values should be used to group this array. If a

(
        self,
        group: GroupInput = None,
        *,
        squeeze: Literal[False] = False,
        restore_coord_dims: bool = False,
        eagerly_compute_group: Literal[False] | None = None,
        **groupers: Grouper,
    )

    @_deprecate_positional_args("v2024.07.0")
    def groupby(
        self,
        group: GroupInput = None,
        *,
        squeeze: Literal[False] = False,
        restore_coord_dims: bool = False,
        eagerly_compute_group: Literal[False] | None = None,
        **groupers: Grouper,
    ) -> DataArrayGroupBy:
        """Returns a DataArrayGroupBy object for performing grouped operations.

        Parameters
        ----------
        group : str or DataArray or IndexVariable or sequence of hashable or mapping of hashable to Grouper
            Array whose unique values should be used to group this array. If a
            Hashable, must be the name of a coordinate contained in this dataarray. If a dictionary,
            must map an existing variable name to a :py:class:`Grouper` instance.
        squeeze : False
            This argument is deprecated.
        restore_coord_dims : bool, default: False
            If True, also restore the dimension order of multi-dimensional
            coordinates.
        eagerly_compute_group: bool, optional
            This argument is deprecated.
        **groupers : Mapping of str to Grouper or Resampler
            Mapping of variable name to group by to :py:class:`Grouper` or :py:class:`Resampler` object.
            One of ``group`` or ``groupers`` must be provided.
            Only a single ``grouper`` is allowed at present.

        Returns
        -------
        grouped : DataArrayGroupBy
            A `DataArrayGroupBy` object patterned after `pandas.GroupBy` that can be
            iterated over in the form of `(unique_value, grouped_array)` pairs.

        Examples
        --------
        Calculate daily anomalies for daily data:

        >>> da = xr.DataArray(
        ...     np.linspace(0, 1826, num=1827),
        ...     coords=[pd.date_range("2000-01-01", "2004-12-31", freq="D")],
        ...     dims="time",
        ... )
        >>> da
        <xarray.DataArray (time: 1827)> Size: 15kB
        array([0.000e+00, 1.000e+00, 2.000e+00, ..., 1.824e+03, 1.825e+03,
               1.826e+03], shape=(1827,))
        Coordinates:
          * time     (time) datetime64[us] 15kB 2000-01-01 2000-01-02 ... 2004-12-31
        >>> da.groupby("time.dayofyear") - da.groupby("time.dayofyear").mean("time")
        <xarray.DataArray (time: 1827)> Size: 15kB
        array([-730.8, -730.8, -730.8, ...,  730.2,  730.2,  730.5], shape=(1827,))
        Coordinates:
          * time       (time) datetime64[us] 15kB 2000-01-01 2000-01-02 ... 2004-12-31
            dayofyear  (time) int64 15kB 1 2 3 4 5 6 7 8 ... 360 361 362 363 364 365 366

        Use a ``Grouper`` object to be more explicit