def _aggregate_series_pure_python(
self, obj: Series, func: Callable
) -> npt.NDArray[np.object_]:
ids, _, ngroups = self.group_info
counts = np.zeros(ngroups, dtype=int)
result = np.empty(ngroups, dtype="O")
initialized = False
# equiv: splitter = self._get_splitter(obj, axis=0)
splitter = get_splitter(obj, ids, ngroups, axis=0)
for i, group in enumerate(splitter):
res = func(group)
res = libreduction.extract_result(res)
if not initialized:
# We only do this validation on the first iteration
libreduction.check_result_array(res, group.dtype)
initialized = True
counts[i] = group.shape[0]
result[i] = res
return result
def _aggregate_series_pure_python(self, obj: Series,
func: F) -> np.ndarray:
# -> np.ndarray[object]
ids, _, ngroups = self.group_info
counts = np.zeros(ngroups, dtype=int)
result = np.empty(ngroups, dtype="O")
initialized = False
# equiv: splitter = self._get_splitter(obj, axis=0)
splitter = get_splitter(obj, ids, ngroups, axis=0)
for i, group in enumerate(splitter):
# Each step of this loop corresponds to
# libreduction._BaseGrouper._apply_to_group
res = func(group)
res = libreduction.extract_result(res)
if not initialized:
# We only do this validation on the first iteration
libreduction.check_result_array(res, group.dtype)
initialized = True
counts[i] = group.shape[0]
result[i] = res
return result
def _aggregate_series_pure_python(self, obj: Series, func: F):
group_index, _, ngroups = self.group_info
counts = np.zeros(ngroups, dtype=int)
result = np.empty(ngroups, dtype="O")
initialized = False
splitter = get_splitter(obj, group_index, ngroups, axis=0)
for label, group in splitter:
# Each step of this loop corresponds to
# libreduction._BaseGrouper._apply_to_group
res = func(group)
res = libreduction.extract_result(res)
if not initialized:
# We only do this validation on the first iteration
libreduction.check_result_array(res, 0)
initialized = True
counts[label] = group.shape[0]
result[label] = res
out = lib.maybe_convert_objects(result, try_float=False)
out = maybe_cast_result(out, obj, numeric_only=True)
return out, counts
def _aggregate_series_pure_python(self, obj: Series, func: F):
group_index, _, ngroups = self.group_info
counts = np.zeros(ngroups, dtype=int)
result = np.empty(ngroups, dtype="O")
initialized = False
splitter = get_splitter(obj, group_index, ngroups, axis=0)
for label, group in splitter:
# Each step of this loop corresponds to
# libreduction._BaseGrouper._apply_to_group
res = func(group)
res = libreduction.extract_result(res)
if not initialized:
# We only do this validation on the first iteration
libreduction.check_result_array(res, 0)
initialized = True
counts[label] = group.shape[0]
result[label] = res
result = lib.maybe_convert_objects(result, try_float=False)
# error: Incompatible types in assignment (expression has type
# "Union[ExtensionArray, ndarray]", variable has type "ndarray")
result = maybe_cast_result( # type: ignore[assignment]
result, obj, numeric_only=True)
return result, counts
