xarray.DataTree.prod (original) (raw)
DataTree.prod(dim=None, *, skipna=None, min_count=None, keep_attrs=None, **kwargs)[source]#
Reduce this DataTree’s data by applying prod
along some dimension(s).
Parameters:
- dim (str,
Iterable
ofHashable
,"..."
or None, default: None) – Name of dimension[s] along which to applyprod
. For e.g.dim="x"
ordim=["x", "y"]
. If “…” or None, 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 calculatingprod
on this object’s data. These could include dask-specific kwargs likesplit_every
.
Returns:
reduced (DataTree) – New DataTree with prod
applied to its data and the indicated dimension(s) removed
Notes
Non-numeric variables will be removed prior to reducing.
Examples
dt = xr.DataTree( ... xr.Dataset( ... data_vars=dict(foo=("time", np.array([1, 2, 3, 0, 2, np.nan]))), ... coords=dict( ... time=( ... "time", ... pd.date_range("2001-01-01", freq="ME", periods=6), ... ), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ), ... ) dt <xarray.DataTree> Group: / Dimensions: (time: 6) 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' Data variables: foo (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan
dt.prod() <xarray.DataTree> Group: / Dimensions: () Data variables: foo float64 8B 0.0
Use skipna
to control whether NaNs are ignored.
dt.prod(skipna=False) <xarray.DataTree> Group: / Dimensions: () Data variables: foo float64 8B nan
Specify min_count
for finer control over when NaNs are ignored.
dt.prod(skipna=True, min_count=2) <xarray.DataTree> Group: / Dimensions: () Data variables: foo float64 8B 0.0