PERF: GroupBy.count

pandas/core/groupby/generic.py

@@ -21,6 +21,7 @@
     Mapping,
     TypeVar,
     Union,
+    cast,
 )
 import warnings
 
@@ -30,7 +31,9 @@
 from pandas._typing import (
     ArrayLike,
     FrameOrSeries,
+    Manager,
     Manager2D,
+    SingleManager,
 )
 from pandas.util._decorators import (
     Appender,
@@ -80,7 +83,6 @@
     Index,
     MultiIndex,
     all_indexes_same,
-    default_index,
 )
 from pandas.core.series import Series
 from pandas.core.util.numba_ import maybe_use_numba
@@ -159,19 +161,21 @@ def pinner(cls):
 class SeriesGroupBy(GroupBy[Series]):
     _apply_allowlist = base.series_apply_allowlist
 
-    def _wrap_agged_manager(self, mgr: Manager2D) -> Series:
-        single = mgr.iget(0)
+    def _wrap_agged_manager(self, mgr: Manager) -> Series:
+        if mgr.ndim == 1:
+            mgr = cast(SingleManager, mgr)
+            single = mgr
+        else:
+            mgr = cast(Manager2D, mgr)
+            single = mgr.iget(0)
         ser = self.obj._constructor(single, name=self.obj.name)
         # NB: caller is responsible for setting ser.index
         return ser
 
-    def _get_data_to_aggregate(self) -> Manager2D:
+    def _get_data_to_aggregate(self) -> SingleManager:
         ser = self._obj_with_exclusions
         single = ser._mgr
-        columns = default_index(1)
-        # Much faster than using ser.to_frame() since we avoid inferring columns
-        #  from scalar
-        return single.to_2d_mgr(columns)
+        return single
 
     def _iterate_slices(self) -> Iterable[Series]:
         yield self._selected_obj

pandas/core/groupby/groupby.py

@@ -1745,6 +1745,8 @@ def count(self) -> Series | DataFrame:
         ids, _, ngroups = self.grouper.group_info
         mask = ids != -1
 
+        is_series = data.ndim == 1
+
         def hfunc(bvalues: ArrayLike) -> ArrayLike:
             # TODO(2DEA): reshape would not be necessary with 2D EAs
             if bvalues.ndim == 1:
@@ -1754,6 +1756,10 @@ def hfunc(bvalues: ArrayLike) -> ArrayLike:
                 masked = mask & ~isna(bvalues)
 
             counted = lib.count_level_2d(masked, labels=ids, max_bin=ngroups, axis=1)
+            if is_series:
+                assert counted.ndim == 2
+                assert counted.shape[0] == 1
+                return counted[0]
             return counted
 
         new_mgr = data.grouped_reduce(hfunc)
@@ -2702,7 +2708,7 @@ def blk_func(values: ArrayLike) -> ArrayLike:
         mgr = self._get_data_to_aggregate()
 
         res_mgr = mgr.grouped_reduce(blk_func, ignore_failures=True)
-        if len(res_mgr.items) != len(mgr.items):
+        if not is_ser and len(res_mgr.items) != len(mgr.items):
             warnings.warn(
                 "Dropping invalid columns in "
                 f"{type(self).__name__}.quantile is deprecated. "
@@ -3134,14 +3140,15 @@ def blk_func(values: ArrayLike) -> ArrayLike:
         obj = self._obj_with_exclusions
 
         # Operate block-wise instead of column-by-column
-        orig_ndim = obj.ndim
+        is_ser = obj.ndim == 1
         mgr = self._get_data_to_aggregate()
 
         if numeric_only:
             mgr = mgr.get_numeric_data()
 
         res_mgr = mgr.grouped_reduce(blk_func, ignore_failures=True)
-        if len(res_mgr.items) != len(mgr.items):
+
+        if not is_ser and len(res_mgr.items) != len(mgr.items):
             howstr = how.replace("group_", "")
             warnings.warn(
                 "Dropping invalid columns in "
@@ -3162,7 +3169,7 @@ def blk_func(values: ArrayLike) -> ArrayLike:
                 # We should never get here
                 raise TypeError("All columns were dropped in grouped_reduce")
 
-        if orig_ndim == 1:
+        if is_ser:
             out = self._wrap_agged_manager(res_mgr)
             out.index = self.grouper.result_index
         else:

pandas/core/internals/base.py

@@ -10,6 +10,7 @@
 )
 
 from pandas._typing import (
+    ArrayLike,
     DtypeObj,
     Shape,
 )
@@ -18,7 +19,10 @@
 from pandas.core.dtypes.cast import find_common_type
 
 from pandas.core.base import PandasObject
-from pandas.core.indexes.api import Index
+from pandas.core.indexes.api import (
+    Index,
+    default_index,
+)
 
 T = TypeVar("T", bound="DataManager")
 
@@ -171,6 +175,23 @@ def setitem_inplace(self, indexer, value) -> None:
         """
         self.array[indexer] = value
 
+    def grouped_reduce(self, func, ignore_failures: bool = False):
+        """
+        ignore_failures : bool, default False
+            Not used; for compatibility with ArrayManager/BlockManager.
+        """
+
+        arr = self.array
+        res = func(arr)
+        index = default_index(len(res))
+
+        mgr = type(self).from_array(res, index)
+        return mgr
+
+    @classmethod
+    def from_array(cls, arr: ArrayLike, index: Index):
+        raise AbstractMethodError(cls)
+
 
 def interleaved_dtype(dtypes: list[DtypeObj]) -> DtypeObj | None:
     """

pandas/tests/resample/test_resample_api.py

@@ -350,11 +350,10 @@ def test_agg():
     expected = pd.concat([a_mean, a_std, b_mean, b_std], axis=1)
     expected.columns = pd.MultiIndex.from_product([["A", "B"], ["mean", "std"]])
     for t in cases:
-        warn = FutureWarning if t in cases[1:3] else None
-        with tm.assert_produces_warning(
-            warn, match="Dropping invalid columns", check_stacklevel=False
-        ):
-            # .var on dt64 column raises and is dropped
+        with tm.assert_produces_warning(None):
+            # .var on dt64 column raises and is dropped, but the path in core.apply
+            #  that it goes through will still suppress a TypeError even
+            #  once the deprecations in the groupby code are enforced
             result = t.aggregate([np.mean, np.std])
         tm.assert_frame_equal(result, expected)