xarray.IndexVariable.std

xarray.IndexVariable.std#

IndexVariable.std(dim=None, *, skipna=None, ddof=0, **kwargs)[source]#

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

Parameters:
  • dim (str, Iterable of Hashable, "..." or None, default: None) – Name of dimension[s] along which to apply std. 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).

  • ddof (int, default: 0) – “Delta Degrees of Freedom”: the divisor used in the calculation is N - ddof, where N represents the number of elements.

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

Returns:

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

See also

numpy.std, dask.array.std, Dataset.std, DataArray.std

Aggregation

User guide on reduction or aggregation operations.

Notes

Non-numeric variables will be removed prior to reducing.

Examples

>>> from xarray.namedarray.core import NamedArray
>>> na = NamedArray(
...     "x",
...     np.array([1, 2, 3, 0, 2, np.nan]),
... )
>>> na
<xarray.NamedArray (x: 6)>
array([ 1.,  2.,  3.,  0.,  2., nan])
>>> na.std()
<xarray.NamedArray ()>
array(1.0198039)

Use skipna to control whether NaNs are ignored.

>>> na.std(skipna=False)
<xarray.NamedArray ()>
array(nan)

Specify ddof=1 for an unbiased estimate.

>>> na.std(skipna=True, ddof=1)
<xarray.NamedArray ()>
array(1.14017543)