xarray.core.resample.DataArrayResample.count
xarray.core.resample.DataArrayResample.count¶
-
DataArrayResample.
count
(dim=None, keep_attrs=None, **kwargs)[source]¶ Reduce this DataArray’s data by applying
count
along some dimension(s).- Parameters
dim (hashable or iterable of hashable, optional) – Name of dimension[s] along which to apply
count
. For e.g.dim="x"
ordim=["x", "y"]
. IfNone
, will reduce over all dimensions present in the grouped variable.keep_attrs (
bool
, optional) – If True,attrs
will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.**kwargs (
dict
) – Additional keyword arguments passed on to the appropriate array function for calculatingcount
on this object’s data.
- Returns
reduced (
DataArray
) – New DataArray withcount
applied to its data and the indicated dimension(s) removed
Examples
>>> da = xr.DataArray( ... np.array([1, 2, 3, 1, 2, np.nan]), ... dims="time", ... coords=dict( ... time=("time", pd.date_range("01-01-2001", freq="M", periods=6)), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ) >>> da <xarray.DataArray (time: 6)> array([ 1., 2., 3., 1., 2., nan]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'
>>> da.resample(time="3M").count() <xarray.DataArray (time: 3)> array([1, 3, 1]) Coordinates: * time (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31
See also
numpy.count
,DataArray.count
- Resampling and grouped operations
User guide on resampling operations.