Variable.load() is a no-op for numpy data; can skip dispatch entirely

[FBumann]

What

For a Variable whose ._data is already an in-memory numpy.ndarray, Variable.load() does no useful work — it walks a dispatch chain that always returns the same array unchanged. The cost is paid once per variable inside Dataset.load() / DataArray.load(), on top of (and after) is_chunked_array has already concluded the variable isn't chunked.

Code path

Variable.load (xarray/core/variable.py:1022):

def load(self, **kwargs) -> Self:
 self._data = to_duck_array(self._data, **kwargs)
 return self

to_duck_array (xarray/namedarray/pycompat.py:139):

def to_duck_array(data, **kwargs):
 if is_chunked_array(data):
 ...
 if isinstance(data, ExplicitlyIndexed | ImplicitToExplicitIndexingAdapter):
 return data.get_duck_array()
 elif is_duck_array(data):
 return data
 else:
 return np.asarray(data)

For np.ndarray:

1. is_chunked_array(data) → False
2. isinstance(data, ExplicitlyIndexed | ImplicitToExplicitIndexingAdapter) → False (np.ndarray is neither)
3. is_duck_array(data) → True → returns data unchanged

So self._data = to_duck_array(self._data) reduces to self._data = self._data. The entire call is dispatch overhead.

Why it matters

Dataset.load() calls Variable.load() per variable. The same dispatch is also reached implicitly by:

• Dataset.compute() / DataArray.compute() — dataset.py:803, dataarray.py:1247
• xr.load_dataset() / xr.load_dataarray() / xr.load_datatree() — backends/api.py:166,192,218
• DataArray.persist() — dataarray.py:1173
• Some backend writer paths — backends/writers.py:768

On many-variable datasets — common with open_mfdataset / concat results — this overhead compounds. (Note: arithmetic does not call .load(). Lazy stays lazy. .values, .to_dataframe(), .plot(), repr etc. materialize data via to_duck_array directly without going through Variable.load, so they are unaffected by this issue.)

Proposed fix

Add a numpy fast-path at the top of Variable.load() and Variable.load_async():

def load(self, **kwargs) -> Self:
 if isinstance(self._data, np.ndarray):
 return self
 self._data = to_duck_array(self._data, **kwargs)
 return self