xarray.Dataset.groupby¶
-
Dataset.
groupby
(group, squeeze=True, restore_coord_dims=None)¶ Returns a GroupBy object for performing grouped operations.
- Parameters
group (
str
,DataArray
orIndexVariable
) – Array whose unique values should be used to group this array. If a string, must be the name of a variable contained in this dataset.squeeze (
bool
, 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_dims (
bool
, optional) – If True, also restore the dimension order of multi-dimensional coordinates.
- Returns
grouped
– A GroupBy 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("1/1/2000", "31/12/2004", freq="D")], ... dims="time", ... ) >>> da <xarray.DataArray (time: 1827)> array([0.000e+00, 1.000e+00, 2.000e+00, ..., 1.824e+03, 1.825e+03, 1.826e+03]) Coordinates: * time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2004-12-31 >>> da.groupby("time.dayofyear") - da.groupby("time.dayofyear").mean("time") <xarray.DataArray (time: 1827)> array([-730.8, -730.8, -730.8, ..., 730.2, 730.2, 730.5]) Coordinates: * time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2004-12-31 dayofyear (time) int64 1 2 3 4 5 6 7 8 ... 359 360 361 362 363 364 365 366