xarray.DataArray.count (original) (raw)

DataArray.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 “…” or None, 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

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.count() <xarray.DataArray ()> Size: 8B array(5)