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 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, 0)
initialized = True
counts[label] = group.shape[0]
result[label] = res
out = lib.maybe_convert_objects(result, try_float=False)
out = maybe_cast_pointwise_result(out, obj.dtype, numeric_only=True)
return out, counts
def agg_series(self, obj: Series, func: F) -> ArrayLike:
# Caller is responsible for checking ngroups != 0
assert self.ngroups != 0
cast_back = True
if len(obj) == 0:
# SeriesGrouper would raise if we were to call _aggregate_series_fast
result = self._aggregate_series_pure_python(obj, func)
elif not isinstance(obj._values, np.ndarray):
# _aggregate_series_fast would raise TypeError when
# calling libreduction.Slider
# In the datetime64tz case it would incorrectly cast to tz-naive
# TODO: can we get a performant workaround for EAs backed by ndarray?
result = self._aggregate_series_pure_python(obj, func)
elif obj.index._has_complex_internals:
# Preempt TypeError in _aggregate_series_fast
result = self._aggregate_series_pure_python(obj, func)
else:
result = self._aggregate_series_fast(obj, func)
cast_back = False
npvalues = lib.maybe_convert_objects(result, try_float=False)
if cast_back:
# TODO: Is there a documented reason why we dont always cast_back?
out = maybe_cast_pointwise_result(npvalues,
obj.dtype,
numeric_only=True)
else:
out = npvalues
return out
def agg_series(
self, obj: Series, func: F, preserve_dtype: bool = False
) -> ArrayLike:
"""
Parameters
----------
obj : Series
func : function taking a Series and returning a scalar-like
preserve_dtype : bool
Whether the aggregation is known to be dtype-preserving.
Returns
-------
np.ndarray or ExtensionArray
"""
# test_groupby_empty_with_category gets here with self.ngroups == 0
# and len(obj) > 0
if len(obj) == 0:
# SeriesGrouper would raise if we were to call _aggregate_series_fast
result = self._aggregate_series_pure_python(obj, func)
elif not isinstance(obj._values, np.ndarray):
# _aggregate_series_fast would raise TypeError when
# calling libreduction.Slider
# In the datetime64tz case it would incorrectly cast to tz-naive
# TODO: can we get a performant workaround for EAs backed by ndarray?
result = self._aggregate_series_pure_python(obj, func)
# we can preserve a little bit more aggressively with EA dtype
# because maybe_cast_pointwise_result will do a try/except
# with _from_sequence. NB we are assuming here that _from_sequence
# is sufficiently strict that it casts appropriately.
preserve_dtype = True
elif obj.index._has_complex_internals:
# Preempt TypeError in _aggregate_series_fast
result = self._aggregate_series_pure_python(obj, func)
elif isinstance(self, BinGrouper):
# Not yet able to remove the BaseGrouper aggregate_series_fast,
# as test_crosstab.test_categorical breaks without it
result = self._aggregate_series_pure_python(obj, func)
else:
result = self._aggregate_series_fast(obj, func)
npvalues = lib.maybe_convert_objects(result, try_float=False)
if preserve_dtype:
out = maybe_cast_pointwise_result(npvalues, obj.dtype, numeric_only=True)
else:
out = npvalues
return out
def agg_series(self,
obj: Series,
func: F,
preserve_dtype: bool = False) -> ArrayLike:
"""
Parameters
----------
obj : Series
func : function taking a Series and returning a scalar-like
preserve_dtype : bool
Whether the aggregation is known to be dtype-preserving.
Returns
-------
np.ndarray or ExtensionArray
"""
# test_groupby_empty_with_category gets here with self.ngroups == 0
# and len(obj) > 0
if len(obj) == 0:
# SeriesGrouper would raise if we were to call _aggregate_series_fast
result = self._aggregate_series_pure_python(obj, func)
elif not isinstance(obj._values, np.ndarray):
result = self._aggregate_series_pure_python(obj, func)
# we can preserve a little bit more aggressively with EA dtype
# because maybe_cast_pointwise_result will do a try/except
# with _from_sequence. NB we are assuming here that _from_sequence
# is sufficiently strict that it casts appropriately.
preserve_dtype = True
else:
result = self._aggregate_series_pure_python(obj, func)
npvalues = lib.maybe_convert_objects(result, try_float=False)
if preserve_dtype:
out = maybe_cast_pointwise_result(npvalues,
obj.dtype,
numeric_only=True)
else:
out = npvalues
return out
