DataArrayRolling.mean() ignores skipna=True kwarg #6772
Description
What happened?
In the following example, it appears that the skipna=True argument is being ignored, unless you call construct before taking the mean.
What did you expect to happen?
I expected:
da.rolling(time=3).mean("time", skipna=True) == da.rolling(time=3).construct("window").mean("window", skipna=True)
Minimal Complete Verifiable Example
import xarray as xr
import numpy as np
da = xr.DataArray([0, 1, 2, np.NAN, 4, 5, 6], dims="time")
# Results 1 and 2 produce the same array, without skipping NANs
result1 = da.rolling(time=3).mean("time", skipna=True)
result2 = da.rolling(time=3).mean("time", skipna=False)
# Results 3 and 4 produce the expected (different) arrays
result3 = da.rolling(time=3).construct("window").mean("window", skipna=True)
result4 = da.rolling(time=3).construct("window").mean("window", skipna=False)
print(result1)
print(result2)
print(result3)
print(result4)MVCE confirmation
- Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
- Complete example — the example is self-contained, including all data and the text of any traceback.
- Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
- New issue — a search of GitHub Issues suggests this is not a duplicate.
Relevant log output
<xarray.DataArray (time: 7)>
array([nan, nan, 1., nan, nan, nan, 5.])
Dimensions without coordinates: time
<xarray.DataArray (time: 7)>
array([nan, nan, 1., nan, nan, nan, 5.])
Dimensions without coordinates: time
<xarray.DataArray (time: 7)>
array([0. , 0.5, 1. , 1.5, 3. , 4.5, 5. ])
Dimensions without coordinates: time
<xarray.DataArray (time: 7)>
array([nan, nan, 1., nan, nan, nan, 5.])
Dimensions without coordinates: timeAnything else we need to know?
Note: the first two NaNs are expected due to the min_periods argument to rolling(), but the middle NaNs are what are unexpected when skipna = True
Environment
INSTALLED VERSIONS
commit: None
python: 3.9.13 | packaged by conda-forge | (main, May 27 2022, 16:50:36) [MSC v.1929 64 bit (AMD64)]
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: AMD64 Family 23 Model 17 Stepping 0, AuthenticAMD
byteorder: little
LC_ALL: None
LANG: None
LOCALE: ('English_United States', '1252')
libhdf5: 1.12.1
libnetcdf: 4.8.1
xarray: 0.19.0
pandas: 1.3.4
numpy: 1.21.2
scipy: 1.7.1
netCDF4: 1.6.0
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: 2.12.0
cftime: 1.6.0
nc_time_axis: None
PseudoNetCDF: None
rasterio: 1.2.10
cfgrib: None
iris: None
bottleneck: None
dask: 2022.6.1
distributed: 2022.6.1
matplotlib: 3.4.3
cartopy: 0.20.1
seaborn: None
numbagg: None
pint: 0.19.2
setuptools: 59.8.0
pip: 22.1.2
conda: None
pytest: 7.1.2
IPython: 8.4.0
sphinx: None