DataArrayGroupBy.sum(dim=None, *, skipna=None, min_count=None, keep_attrs=None, **kwargs)[source]#

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

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

  • skipna (bool or None, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).

  • min_count (int or None, optional) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Only used if skipna is set to True or defaults to True for the array’s dtype. Changed in version 0.17.0: if specified on an integer array and skipna=True, the result will be a float array.

  • 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 sum on this object’s data. These could include dask-specific kwargs like split_every.


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

See also

numpy.sum, dask.array.sum, DataArray.sum

GroupBy: Group and Bin Data

User guide on groupby operations.


Non-numeric variables will be removed prior to reducing.


>>> 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])
  * time     (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'
>>> da.groupby("labels").sum()
<xarray.DataArray (labels: 3)>
array([1., 4., 4.])
  * labels   (labels) object 'a' 'b' 'c'

Use skipna to control whether NaNs are ignored.

>>> da.groupby("labels").sum(skipna=False)
<xarray.DataArray (labels: 3)>
array([nan,  4.,  4.])
  * labels   (labels) object 'a' 'b' 'c'

Specify min_count for finer control over when NaNs are ignored.

>>> da.groupby("labels").sum(skipna=True, min_count=2)
<xarray.DataArray (labels: 3)>
array([nan,  4.,  4.])
  * labels   (labels) object 'a' 'b' 'c'