DataArrray.resample() appears to ignore skipna parameter while taking annual mean

[arfriedman]

What happened?

The skipna parameter appears to have no effect while annually resampling a monthly DataArray.
I'm not sure if this is related to #6772

What did you expect to happen?

I expected da_yrA to be NaN since I am not skipping NaN values.

Minimal Complete Verifiable Example

import numpy as np
import pandas as pd
import xarray as xr

data = np.arange(12)
month_index = pd.date_range("2022-01","2022-12-31", freq="M")
df = pd.DataFrame(index=month_index, data=data)
df.index.name = "time"
df.columns.name = "value"
df.loc["2022-01":"2022-02"] = np.nan
da = xr.DataArray(df)

# these unexpectedly produce the same result
da_yrA = (da).resample(time="A-Dec", skipna=False).mean()
da_yrB = (da).resample(time="A-Dec", skipna=True).mean()

print(da_yrA)
print(da_yrB)

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

No response

Anything else we need to know?

No response

Environment

scipy: 1.10.0
netCDF4: 1.6.2
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: None
cftime: 1.6.2
nc_time_axis: 1.4.1
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: None
dask: 2023.2.0
distributed: 2023.2.0
matplotlib: 3.7.0
cartopy: 0.21.1
seaborn: None
numbagg: None
fsspec: 2023.1.0
cupy: None
pint: None
sparse: 0.13.0
flox: None
numpy_groupies: None
setuptools: 67.3.2
pip: 23.0.1
conda: None
pytest: None
mypy: None
IPython: 8.10.0
sphinx: None

[keewis]

It seems that skipna has been ignored since #2541 (more than four years ago). While that might have been unintentional at the time I don't think resample should have that option, so maybe we should just raise a warning like we do for keep_attrs and eventually remove both.

Does this do what you expect?

In [2]: da.resample(time="A-Dec").mean(skipna=True)
Out[2]: 
<xarray.DataArray (time: 1, value: 1)>
array([[6.5]])
Coordinates:
  * value    (value) int64 0
  * time     (time) datetime64[ns] 2022-12-31

In [3]: da.resample(time="A-Dec").mean(skipna=False)
Out[3]: 
<xarray.DataArray (time: 1, value: 1)>
array([[nan]])
Coordinates:
  * value    (value) int64 0
  * time     (time) datetime64[ns] 2022-12-31

Edit: I don't think this is related to #6772, but I'll also have a look at what's wrong there

[arfriedman]

Thanks @keewis -- your suggestion does what I expect.

[dcherian]

I think we should raise and let the user know to provide skipna, keep_attrs etc. in the actual reduction method, not the groupby.