xarray.core.resample.DatasetResample.std#

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

Reduce this Dataset’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 None, will reduce over the Resample 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).

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

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

Returns:

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

See also

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

Resampling and grouped operations

User guide on resampling operations.

Notes

Use the flox package to significantly speed up resampling computations, especially with dask arrays. Xarray will use flox by default if installed. Pass flox-specific keyword arguments in **kwargs. The default choice is method="cohorts" which generalizes the best, method="blockwise" might work better for your problem. See the flox documentation for more.

Non-numeric variables will be removed prior to reducing.

Examples

>>> da = xr.DataArray(
...     np.array([1, 2, 3, 1, 2, np.nan]),
...     dims="time",
...     coords=dict(
...         time=("time", pd.date_range("2001-01-01", freq="M", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> ds = xr.Dataset(dict(da=da))
>>> ds
<xarray.Dataset>
Dimensions:  (time: 6)
Coordinates:
  * time     (time) datetime64[ns] 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
    da       (time) float64 1.0 2.0 3.0 1.0 2.0 nan

>>> ds.resample(time="3M").std()
<xarray.Dataset>
Dimensions:  (time: 3)
Coordinates:
  * time     (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31
Data variables:
    da       (time) float64 0.0 0.8165 0.0

Use skipna to control whether NaNs are ignored.

>>> ds.resample(time="3M").std(skipna=False)
<xarray.Dataset>
Dimensions:  (time: 3)
Coordinates:
  * time     (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31
Data variables:
    da       (time) float64 0.0 0.8165 nan

Specify ddof=1 for an unbiased estimate.

>>> ds.resample(time="3M").std(skipna=True, ddof=1)
<xarray.Dataset>
Dimensions:  (time: 3)
Coordinates:
  * time     (time) datetime64[ns] 2001-01-31 2001-04-30 2001-07-31
Data variables:
    da       (time) float64 nan 1.0 nan