issue_comments

Hi @mathause

It does work if the array keeps the size:

python data.resample(index="M").apply(lambda x: x.values)

Thanks, but I am not sure I find that intuitive, why should the resampled array have the same size as the original? It seems to make only sense for DataArray.apply(), but not for a resampled one. As I indicated in my other reply, returning a scalar or equivalent should be fine, shouldn't it? At the very least the documentation is lacking, it refers to the pandas method, but clearly the behaviour is different.

As a workaround you could allow your function to consume a dummy axis. Or you could pass dim as ...

python data.resample(index="M").reduce(lambda x, axis: 1) # workaround 1 data.resample(index="M").reduce(lambda x: 1, dim=...) # workaround 2

(reduce only passes axis if dim is not None but groupby passes the group_dim per default.

That feels a bit like curing the symptoms instead of the root cause, why not set dim='...' if it is not given?

It does work if the array keeps the size:

python data.resample(index="M").apply(lambda x: x.values)

As a workaround you could allow your function to consume a dummy axis. Or you could pass dim as ...

python data.resample(index="M").reduce(lambda x, axis: 1) # workaround 1 data.resample(index="M").reduce(lambda x: 1, dim=...) # workaround 2

(reduce only passes axis if dim is not None but groupby passes the group_dim per default.

@mathause Thanks, that works for np.median, but .reduce() passes an axis= keyword argument, which needs to be taken care of by any custom function. I used np.median here just as an example, as it showed the problem.

@dcherian I am not sure if raising an error would be very user friendly. In my opinion, which may be biased by my personal use cases, I would expect .apply() (or .map() for that matter) of a resample object to take any function that returns a scalar: the value at the resampled point. It should be then up to xarray to iterate over any other non-resampled dimensions. I am not sure why it requires to return a DataArray in the first place. As mentioned, it works with pandas series and dataframes, e.g.:

python data.to_pandas().resample("M").apply(np.median)

1114 applied_example, applied = peek_at(applied)

We could raise an error here if applied_example is not a DataArray or Dataset but not sure if its worth it. This is a somewhat common user question

apply seems to expect that the applied function returns a DataArray. As an alternative could you use data.resample(index="M").reduce(np.median)?