def min(self, *, skipna=True, axis: int | None = 0, **kwargs):
nv.validate_min((), kwargs)
return masked_reductions.min(
self._data,
self._mask,
skipna=skipna,
axis=axis,
)
def min(self, axis=None, skipna: bool = True, **kwargs) -> Scalar:
nv.validate_min((), kwargs)
result = masked_reductions.min(values=self.to_numpy(),
mask=self.isna(),
skipna=skipna)
return self._wrap_reduction_result(axis, result)
def min(self, *, skipna: bool = True, **kwargs) -> Scalar:
nv.validate_min((), kwargs)
return masked_reductions.min(
values=self.to_numpy(), mask=self.isna(), skipna=skipna
)
