issue_comments: 411705656

Thanks, @fujiisoup .

I have good news and i have bad news.

A) Your min_count argument still seems to work only if using the new syntax for resample, i.e. data.resample($dim=$freq).sum(). I guess, this is due to the cancellation of the old syntax in the future. Using the old syntax data.resample(dim=$dim,freq=$freq,how=$oper) your code seems to ignore the min_count argument.

B) Your min_count argument is not allowed for type 'dataset' but only for type 'dataarray'. Starting with the dataset located here: https://swiftbrowser.dkrz.de/public/dkrz_c0725fe8741c474b97f291aac57f268f/GregorMoeller/ I've got the following message: data.resample(time="M").sum(min_count=1) TypeError: sum() got an unexpected keyword argument 'min_count'

Thus, I have tested your implementation only on dataarrays. I take the netcdf - array 'TOT_PREC' and try to compute the monthly sum: In [39]: data = array.open_dataset("eObs_gridded_0.22deg_rot_v14.0.TOT_PREC.1950-2016.nc_CutParamTimeUnitCor_FinalEvalGrid") In [40]: datamonth = data["TOT_PREC"].resample(time="M").sum() In [41]: datamonth Out[41]: <xarray.DataArray 'TOT_PREC' (time: 5)> array([ 551833.25 , 465640.09375, 328445.90625, 836892.1875 , 503601.5 ], dtype=float32) Coordinates: time (time) datetime64[ns] 2006-05-31 2006-06-30 2006-07-31 ...

So, the xarray-package is still throwing away the dimensions in x- and y-direction. It has nothing to do with any min-count argument. THIS MUST BE A BUG OF XARRAY. The afore-mentioned dimensions only survive using the old-syntax: ``` In [41]: datamonth = data["TOT_PREC"].resample(dim="time",freq="M",how="sum") /usr/bin/ipython3:1: FutureWarning: .resample() has been modified to defer calculations. Instead of passing 'dim' and how="sum", instead consider using .resample(time="M").sum('time') #!/usr/bin/env python3

In [42]: datamonth Out[42]: <xarray.DataArray 'TOT_PREC' (time: 5, rlat: 136, rlon: 144)> array([[[ 0. , 0. , ..., 0. , 0. ], [ 0. , 0. , ..., 0. , 0. ], ..., [ 0. , 0. , ..., 44.900028, 41.400024], [ 0. , 0. , ..., 49.10001 , 46.5 ]]], dtype=float32) Coordinates: * time (time) datetime64[ns] 2006-05-31 2006-06-30 2006-07-31 ... * rlon (rlon) float32 -22.6 -22.38 -22.16 -21.94 -21.72 -21.5 -21.28 ... * rlat (rlat) float32 -12.54 -12.32 -12.1 -11.88 -11.66 -11.44 -11.22 ...

```

Nevertheless, I have started to use your min_count argument only at one point (the x- and y-dimensions do not matter). In that case, your implementation works fine: ``` In [46]: pointdata = data.isel(rlon=10,rlat=10) In [47]: pointdata["TOT_PREC"] Out[47]: <xarray.DataArray 'TOT_PREC' (time: 153)> array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, ... nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], dtype=float32) Coordinates: rlon float32 -20.4 rlat float32 -10.34 * time (time) datetime64[ns] 2006-05-01T12:00:00 2006-05-02T12:00:00 ... Attributes: standard_name: precipitation_amount long_name: Precipitation units: kg m-2 grid_mapping: rotated_pole cell_methods: time: sum

In [48]: pointdata["TOT_PREC"].resample(time="M").sum(min_count=1) Out[48]: <xarray.DataArray 'TOT_PREC' (time: 5)> array([ nan, nan, nan, nan, nan]) Coordinates: * time (time) datetime64[ns] 2006-05-31 2006-06-30 2006-07-31 ... rlon float32 -20.4 rlat float32 -10.34

In [49]: pointdata["TOT_PREC"].resample(time="M").sum(min_count=0) Out[49]: <xarray.DataArray 'TOT_PREC' (time: 5)> array([ 0., 0., 0., 0., 0.], dtype=float32) Coordinates: * time (time) datetime64[ns] 2006-05-31 2006-06-30 2006-07-31 ... rlon float32 -20.4 rlat float32 -10.34

```