xarray.DataArray.groupby

xarray.DataArray.groupby#

DataArray.groupby(group=None, *, squeeze=False, restore_coord_dims=False, **groupers)[source]#

Returns a DataArrayGroupBy object for performing grouped operations.

Parameters:

  • group (Hashable or DataArray or IndexVariable 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 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.

  • **groupers (Mapping of str to Grouper or Resampler) – Mapping of variable name to group by to Grouper or 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])
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

Use a Grouper object to be more explicit

>>> da.coords["dayofyear"] = da.time.dt.dayofyear
>>> da.groupby(dayofyear=xr.groupers.UniqueGrouper()).mean()
<xarray.DataArray (dayofyear: 366)> Size: 3kB
array([ 730.8,  731.8,  732.8, ..., 1093.8, 1094.8, 1095.5])
Coordinates:
  * dayofyear  (dayofyear) int64 3kB 1 2 3 4 5 6 7 ... 361 362 363 364 365 366

See also

GroupBy: Group and Bin Data

Users guide explanation of how to group and bin data.

Computational Patterns

Tutorial on Groupby() for windowed computation

Grouped Computations

Tutorial on Groupby() demonstrating reductions, transformation and comparison with resample()

DataArray.groupby_bins Dataset.groupby core.groupby.DataArrayGroupBy DataArray.coarsen pandas.DataFrame.groupby Dataset.resample DataArray.resample

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