xarray.core.resample.DatasetResample.median

xarray.core.resample.DatasetResample.median#

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

Reduce this Dataset’s data by applying median along some dimension(s).

Parameters

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

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

Returns

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

See also

numpy.median, dask.array.median, Dataset.median

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. See the flox documentation for more.

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"])),
...     ),
... )
>>> ds = xr.Dataset(dict(da=da))
>>> ds
<xarray.Dataset> Size: 120B
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:
    da       (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan

>>> ds.resample(time="3ME").median()
<xarray.Dataset> Size: 48B
Dimensions:  (time: 3)
Coordinates:
  * time     (time) datetime64[ns] 24B 2001-01-31 2001-04-30 2001-07-31
Data variables:
    da       (time) float64 24B 1.0 2.0 2.0

Use skipna to control whether NaNs are ignored.

>>> ds.resample(time="3ME").median(skipna=False)
<xarray.Dataset> Size: 48B
Dimensions:  (time: 3)
Coordinates:
  * time     (time) datetime64[ns] 24B 2001-01-31 2001-04-30 2001-07-31
Data variables:
    da       (time) float64 24B 1.0 2.0 nan
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