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 )