What should `Dataset.count` return for missing dims?

What is your issue?

When using a dataset with multiple variables and using Dataset.count("x") it will return ones for variables that are missing dimension “x”, e.g.:

import xarray as xr
ds = xr.Dataset({"a": ("x", [1, 2, 3]), "b": ("y", [4, 5])})
ds.count("x")
# returns:
# <xarray.Dataset>
# Dimensions:  (y: 2)
# Dimensions without coordinates: y
# Data variables:
#     a        int32 3
#     b        (y) int32 1 1

I can understand why “1” can be a valid answer, but the result is probably a bit philosophical.

For my usecase I would like it to return an array of ds.sizes["x"] / 0. I think this is also a valid return value, considering the broadcasting rules, where the size of the missing dimension is actually known in the dataset.

Maybe one could make this behavior adjustable with a kwarg, e.g. "missing_dim_value: {int, “size”}, default 1.

[dcherian]

We discussed:

1. dropping variables without the dimension
2. Return ds.sizes[“x”] by broadcasting b along x

---

For the other reductions

import numpy as np
import xarray as xr

from xarray.core.duck_array_ops import count

ds = xr.Dataset({"a": ("x", [1, 2, 3]), "b": ("y", [4, 5])})

for func in [np.nansum, np.nanprod, np.nanmean, np.nanvar, np.nanstd, count]:
    print(f"{func.__name__!s}({ds.b.data}, axis=()) = {func(ds.b.data, axis=())}")

gives

nansum([4 5], axis=()) = [4 5]
nanprod([4 5], axis=()) = [4 5]
nanmean([4 5], axis=()) = [4. 5.]
nanvar([4 5], axis=()) = [0. 0.]
nanstd([4 5], axis=()) = [0. 0.]
count([4 5], axis=()) = [1 1]

I guess the output for nansum, nanprod doesn’t match what you would get by broadcasting along the absent dimension.

[headtr1ck]

Another option is to add an option: missing_dim: “raise”, ignore" or “broadcast”. The default then would be ignore, which is the current implementation.

But for workflows of variables that are either DataArray or Dataset, this argument should be added to DataArray.sum/count/prod as well?