xarray.DataArray.prod#
- DataArray.prod(dim=None, *, skipna=None, min_count=None, keep_attrs=None, **kwargs)[source]#
Reduce this DataArray’s data by applying
prod
along some dimension(s).- Parameters
dim (
str
,Iterable
ofHashable
,"..."
orNone
, 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
orNone
, 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) orskipna=True
has not been implemented (object, datetime64 or timedelta64).min_count (
int
orNone
, 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
orNone
, 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 (
DataArray
) – New DataArray withprod
applied to its data and the indicated dimension(s) removed
See also
numpy.prod
,dask.array.prod
,Dataset.prod
- Aggregation
User guide on reduction or aggregation operations.
Notes
Non-numeric variables will be removed prior to reducing.
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.prod() <xarray.DataArray ()> Size: 8B array(0.)
Use
skipna
to control whether NaNs are ignored.>>> da.prod(skipna=False) <xarray.DataArray ()> Size: 8B array(nan)
Specify
min_count
for finer control over when NaNs are ignored.>>> da.prod(skipna=True, min_count=2) <xarray.DataArray ()> Size: 8B array(0.)