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Variable.prod(dim=None, *, skipna=None, min_count=None, **kwargs)[source]#

Reduce this NamedArray’s data by applying prod along some dimension(s).

  • dim (str, Iterable of Hashable, "..." or None, default: None) – Name of dimension[s] along which to apply prod. For e.g. dim="x" or dim=["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.

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


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

See also

numpy.prod, dask.array.prod, Dataset.prod, DataArray.prod


User guide on reduction or aggregation operations.


Non-numeric variables will be removed prior to reducing.


>>> from xarray.namedarray.core import NamedArray
>>> na = NamedArray(
...     "x",
...     np.array([1, 2, 3, 0, 2, np.nan]),
... )
>>> na
<xarray.NamedArray (x: 6)> Size: 48B
array([ 1.,  2.,  3.,  0.,  2., nan])
>>> na.prod()
<xarray.NamedArray ()> Size: 8B

Use skipna to control whether NaNs are ignored.

>>> na.prod(skipna=False)
<xarray.NamedArray ()> Size: 8B

Specify min_count for finer control over when NaNs are ignored.

>>> na.prod(skipna=True, min_count=2)
<xarray.NamedArray ()> Size: 8B