xarray.core.groupby.DataArrayGroupBy.count#

DataArrayGroupBy.count(dim=None, *, keep_attrs=None, **kwargs)[source]#

Reduce this DataArray’s data by applying count along some dimension(s).

Parameters:

  • dim (str, Iterable of Hashable, "..." or None, default: None) – Name of dimension[s] along which to apply count. For e.g. dim="x" or dim=["x", "y"]. If None, will reduce over the GroupBy dimensions. If “…”, will reduce over all dimensions.

  • keep_attrs (bool or None, optional) – If True, attrs will be copied from the original object to the new one. If False, the new object will be returned without attributes.

  • **kwargs (Any) – Additional keyword arguments passed on to the appropriate array function for calculating count on this object’s data. These could include dask-specific kwargs like split_every.

Returns:

reduced (DataArray) – New DataArray with count applied to its data and the indicated dimension(s) removed

See also

pandas.DataFrame.count, dask.dataframe.DataFrame.count, DataArray.count

GroupBy: Group and Bin Data

User guide on groupby operations.

Notes

Use the flox package to significantly speed up groupby computations, especially with dask arrays. Xarray will use flox by default if installed. Pass flox-specific keyword arguments in **kwargs. See the flox documentation for more.

Examples

>>> da = xr.DataArray(
...     np.array([1, 2, 3, 0, 2, np.nan]),
...     dims="time",
...     coords=dict(
...         time=("time", pd.date_range("2001-01-01", freq="ME", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> da
<xarray.DataArray (time: 6)> Size: 48B
array([ 1.,  2.,  3.,  0.,  2., nan])
Coordinates:
  * time     (time) datetime64[ns] 48B 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'

>>> da.groupby("labels").count()
<xarray.DataArray (labels: 3)> Size: 24B
array([1, 2, 2])
Coordinates:
  * labels   (labels) object 24B 'a' 'b' 'c'