groupby

UnitsAwareDataArray.groupby(group: Hashable | DataArray | IndexVariable, squeeze: bool | None = None, restore_coord_dims: bool = False) → DataArrayGroupBy

Returns a DataArrayGroupBy object for performing grouped operations.

Parameters:

group (Hashable, DataArray or IndexVariable) – 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.
squeeze (bool, default: True) – 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_dims (bool, default: False) – If True, also restore the dimension order of multi-dimensional coordinates.

Returns:

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

Return type:

DataArrayGroupBy

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])
Coordinates:
  * time     (time) datetime64[ns] 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])
Coordinates:
  * time       (time) datetime64[ns] 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