def test_series_grouper_requires_nonempty_raises():
# GH#29500
obj = Series(np.random.randn(10))
dummy = obj.iloc[:0]
labels = np.array([-1, -1, -1, 0, 0, 0, 1, 1, 1, 1], dtype=np.intp)
with pytest.raises(ValueError, match="SeriesGrouper requires non-empty `series`"):
libreduction.SeriesGrouper(dummy, np.mean, labels, 2)
def test_series_grouper():
obj = Series(np.random.randn(10))
labels = np.array([-1, -1, -1, 0, 0, 0, 1, 1, 1, 1], dtype=np.intp)
grouper = libreduction.SeriesGrouper(obj, np.mean, labels, 2)
result, counts = grouper.get_result()
expected = np.array([obj[3:6].mean(), obj[6:].mean()])
tm.assert_almost_equal(result, expected)
exp_counts = np.array([3, 4], dtype=np.int64)
tm.assert_almost_equal(counts, exp_counts)
def test_series_grouper_result_length_difference():
# GH 40014
obj = Series(np.random.randn(10), dtype="float64")
obj.index = obj.index.astype("O")
labels = np.array([-1, -1, -1, 0, 0, 0, 1, 1, 1, 1], dtype=np.intp)
grouper = libreduction.SeriesGrouper(obj, lambda x: all(x > 0), labels, 2)
result, counts = grouper.get_result()
expected = np.array([all(obj[3:6] > 0), all(obj[6:] > 0)], dtype=object)
tm.assert_equal(result, expected)
exp_counts = np.array([3, 4], dtype=np.int64)
tm.assert_equal(counts, exp_counts)
def test_series_grouper():
from pandas import Series
obj = Series(np.random.randn(10))
dummy = obj[:0]
labels = np.array([-1, -1, -1, 0, 0, 0, 1, 1, 1, 1], dtype=np.int64)
grouper = reduction.SeriesGrouper(obj, np.mean, labels, 2, dummy)
result, counts = grouper.get_result()
expected = np.array([obj[3:6].mean(), obj[6:].mean()])
assert_almost_equal(result, expected)
exp_counts = np.array([3, 4], dtype=np.int64)
assert_almost_equal(counts, exp_counts)
def _aggregate_series_fast(self, obj, func):
# At this point we have already checked that obj.index is not a MultiIndex
# and that obj is backed by an ndarray, not ExtensionArray
func = self._is_builtin_func(func)
group_index, _, ngroups = self.group_info
# avoids object / Series creation overhead
dummy = obj._get_values(slice(None, 0))
indexer = get_group_index_sorter(group_index, ngroups)
obj = obj.take(indexer)
group_index = algorithms.take_nd(group_index, indexer, allow_fill=False)
grouper = libreduction.SeriesGrouper(obj, func, group_index, ngroups, dummy)
result, counts = grouper.get_result()
return result, counts
def _aggregate_series_fast(self, obj, func):
func = self._is_builtin_func(func)
if obj.index._has_complex_internals:
raise TypeError("Incompatible index for Cython grouper")
group_index, _, ngroups = self.group_info
# avoids object / Series creation overhead
dummy = obj._get_values(slice(None, 0))
indexer = get_group_index_sorter(group_index, ngroups)
obj = obj.take(indexer)
group_index = algorithms.take_nd(group_index, indexer, allow_fill=False)
grouper = libreduction.SeriesGrouper(obj, func, group_index, ngroups, dummy)
result, counts = grouper.get_result()
return result, counts
def _aggregate_series_fast(self, obj: Series, func: F) -> npt.NDArray[np.object_]:
# At this point we have already checked that
# - obj.index is not a MultiIndex
# - obj is backed by an ndarray, not ExtensionArray
# - len(obj) > 0
func = com.is_builtin_func(func)
ids, _, ngroups = self.group_info
# avoids object / Series creation overhead
indexer = get_group_index_sorter(ids, ngroups)
obj = obj.take(indexer)
ids = ids.take(indexer)
sgrouper = libreduction.SeriesGrouper(obj, func, ids, ngroups)
result, _ = sgrouper.get_result()
return result
def _aggregate_series_fast(self, obj: Series, func: F):
# At this point we have already checked that
# - obj.index is not a MultiIndex
# - obj is backed by an ndarray, not ExtensionArray
# - len(obj) > 0
# - ngroups != 0
func = self._is_builtin_func(func)
group_index, _, ngroups = self.group_info
# avoids object / Series creation overhead
indexer = get_group_index_sorter(group_index, ngroups)
obj = obj.take(indexer)
group_index = group_index.take(indexer)
grouper = libreduction.SeriesGrouper(obj, func, group_index, ngroups)
result, counts = grouper.get_result()
return result, counts
def _aggregate_series_fast(self, obj, func):
func = self._is_builtin_func(func)
# TODO: pre-empt this, also pre-empt get_result raising TypError if we pass a EA
# for EAs backed by ndarray we may have a performant workaround
if obj.index._has_complex_internals:
raise TypeError("Incompatible index for Cython grouper")
group_index, _, ngroups = self.group_info
# avoids object / Series creation overhead
dummy = obj._get_values(slice(None, 0))
indexer = get_group_index_sorter(group_index, ngroups)
obj = obj.take(indexer)
group_index = algorithms.take_nd(group_index, indexer, allow_fill=False)
grouper = libreduction.SeriesGrouper(obj, func, group_index, ngroups, dummy)
result, counts = grouper.get_result()
return result, counts
