def _reduce(self, name: str, skipna: bool = True, **kwargs):
data = self._data
mask = self._mask
if name == "sum":
return masked_reductions.sum(data, mask, skipna=skipna, **kwargs)
# coerce to a nan-aware float if needed
# (we explicitly use NaN within reductions)
if self._hasna:
data = self.to_numpy("float64", na_value=np.nan)
op = getattr(nanops, "nan" + name)
result = op(data, axis=0, skipna=skipna, mask=mask, **kwargs)
if np.isnan(result):
return libmissing.NA
# if we have a boolean op, don't coerce
if name in ["any", "all"]:
pass
# if we have a preservable numeric op,
# provide coercion back to an integer type if possible
elif name in ["min", "max", "prod"]:
# GH#31409 more performant than casting-then-checking
result = com.cast_scalar_indexer(result)
return result
def sum(self, skipna=True, min_count=0, **kwargs):
nv.validate_sum((), kwargs)
result = masked_reductions.sum(values=self._data,
mask=self._mask,
skipna=skipna,
min_count=min_count)
return result
def _reduce(self, name: str, skipna: bool = True, **kwargs):
if name in {"any", "all"}:
return getattr(self, name)(skipna=skipna, **kwargs)
data = self._data
mask = self._mask
if name == "sum":
return masked_reductions.sum(data, mask, skipna=skipna, **kwargs)
# coerce to a nan-aware float if needed
if self._hasna:
data = self.to_numpy("float64", na_value=np.nan)
op = getattr(nanops, "nan" + name)
result = op(data, axis=0, skipna=skipna, mask=mask, **kwargs)
if np.isnan(result):
return libmissing.NA
# if we have numeric op that would result in an int, coerce to int if possible
if name == "prod" and notna(result):
int_result = np.int64(result)
if int_result == result:
result = int_result
elif name in ["min", "max"] and notna(result):
result = np.bool_(result)
return result
def sum(self, *, skipna=True, min_count=0, axis: int | None = 0, **kwargs):
nv.validate_sum((), kwargs)
# TODO: do this in validate_sum?
if "out" in kwargs:
# np.sum; test_floating_array_numpy_sum
if kwargs["out"] is not None:
raise NotImplementedError
kwargs.pop("out")
result = masked_reductions.sum(
self._data,
self._mask,
skipna=skipna,
min_count=min_count,
axis=axis,
)
return self._wrap_reduction_result(
"sum", result, skipna=skipna, axis=axis, **kwargs
)
