[ArrayManager] GroupBy cython aggregations (no fallback)

.github/workflows/ci.yml

@@ -157,6 +157,7 @@ jobs:
         pytest pandas/tests/reductions/ --array-manager
         pytest pandas/tests/generic/test_generic.py --array-manager
         pytest pandas/tests/arithmetic/ --array-manager
+        pytest pandas/tests/groupby/aggregate/ --array-manager
         pytest pandas/tests/reshape/merge --array-manager
 
         # indexing subset (temporary since other tests don't pass yet)

pandas/core/groupby/generic.py

@@ -41,6 +41,7 @@
     ArrayLike,
     FrameOrSeries,
     FrameOrSeriesUnion,
+    Manager,
 )
 from pandas.util._decorators import (
     Appender,
@@ -107,7 +108,10 @@
     all_indexes_same,
 )
 import pandas.core.indexes.base as ibase
-from pandas.core.internals import BlockManager
+from pandas.core.internals import (
+    ArrayManager,
+    BlockManager,
+)
 from pandas.core.series import Series
 from pandas.core.util.numba_ import maybe_use_numba
 
@@ -1074,20 +1078,22 @@ def _iterate_slices(self) -> Iterable[Series]:
     def _cython_agg_general(
         self, how: str, alt=None, numeric_only: bool = True, min_count: int = -1
     ) -> DataFrame:
-        agg_mgr = self._cython_agg_blocks(
+        agg_mgr = self._cython_agg_manager(
             how, alt=alt, numeric_only=numeric_only, min_count=min_count
         )
         return self._wrap_agged_manager(agg_mgr)
 
-    def _cython_agg_blocks(
+    def _cython_agg_manager(
         self, how: str, alt=None, numeric_only: bool = True, min_count: int = -1
-    ) -> BlockManager:
+    ) -> Manager:
 
-        data: BlockManager = self._get_data_to_aggregate()
+        data: Manager = self._get_data_to_aggregate()
 
         if numeric_only:
             data = data.get_numeric_data(copy=False)
 
+        using_array_manager = isinstance(data, ArrayManager)
+
         def cast_agg_result(result, values: ArrayLike, how: str) -> ArrayLike:
             # see if we can cast the values to the desired dtype
             # this may not be the original dtype
@@ -1101,7 +1107,11 @@ def cast_agg_result(result, values: ArrayLike, how: str) -> ArrayLike:
                 result = type(values)._from_sequence(result.ravel(), dtype=values.dtype)
                 # Note this will have result.dtype == dtype from above
 
-            elif isinstance(result, np.ndarray) and result.ndim == 1:
+            elif (
+                not using_array_manager
+                and isinstance(result, np.ndarray)
+                and result.ndim == 1
+            ):
                 # We went through a SeriesGroupByPath and need to reshape
                 # GH#32223 includes case with IntegerArray values
                 result = result.reshape(1, -1)
@@ -1153,11 +1163,11 @@ def py_fallback(bvalues: ArrayLike) -> ArrayLike:
                 result = mgr.blocks[0].values
                 return result
 
-        def blk_func(bvalues: ArrayLike) -> ArrayLike:
+        def array_func(values: ArrayLike) -> ArrayLike:
 
             try:
                 result = self.grouper._cython_operation(
-                    "aggregate", bvalues, how, axis=1, min_count=min_count
+                    "aggregate", values, how, axis=1, min_count=min_count
                 )
             except NotImplementedError:
                 # generally if we have numeric_only=False
@@ -1170,14 +1180,14 @@ def blk_func(bvalues: ArrayLike) -> ArrayLike:
                     assert how == "ohlc"
                     raise
 
-                result = py_fallback(bvalues)
+                result = py_fallback(values)
 
-            return cast_agg_result(result, bvalues, how)
+            return cast_agg_result(result, values, how)
 
         # TypeError -> we may have an exception in trying to aggregate
         #  continue and exclude the block
         # NotImplementedError -> "ohlc" with wrong dtype
-        new_mgr = data.grouped_reduce(blk_func, ignore_failures=True)
+        new_mgr = data.grouped_reduce(array_func, ignore_failures=True)
 
         if not len(new_mgr):
             raise DataError("No numeric types to aggregate")
@@ -1670,7 +1680,7 @@ def _wrap_frame_output(self, result, obj: DataFrame) -> DataFrame:
         else:
             return self.obj._constructor(result, index=obj.index, columns=result_index)
 
-    def _get_data_to_aggregate(self) -> BlockManager:
+    def _get_data_to_aggregate(self) -> Manager:
         obj = self._obj_with_exclusions
         if self.axis == 1:
             return obj.T._mgr
@@ -1755,17 +1765,17 @@ def _wrap_transformed_output(
 
         return result
 
-    def _wrap_agged_manager(self, mgr: BlockManager) -> DataFrame:
+    def _wrap_agged_manager(self, mgr: Manager) -> DataFrame:
         if not self.as_index:
             index = np.arange(mgr.shape[1])
-            mgr.axes[1] = ibase.Index(index)
+            mgr.set_axis(1, ibase.Index(index), verify_integrity=False)
             result = self.obj._constructor(mgr)
 
             self._insert_inaxis_grouper_inplace(result)
             result = result._consolidate()
         else:
             index = self.grouper.result_index
-            mgr.axes[1] = index
+            mgr.set_axis(1, index, verify_integrity=False)
             result = self.obj._constructor(mgr)
 
         if self.axis == 1:

pandas/core/internals/array_manager.py

@@ -150,18 +150,20 @@ def _normalize_axis(axis):
         axis = 1 if axis == 0 else 0
         return axis
 
-    # TODO can be shared
-    def set_axis(self, axis: int, new_labels: Index) -> None:
+    def set_axis(
+        self, axis: int, new_labels: Index, verify_integrity: bool = True
+    ) -> None:
         # Caller is responsible for ensuring we have an Index object.
         axis = self._normalize_axis(axis)
-        old_len = len(self._axes[axis])
-        new_len = len(new_labels)
+        if verify_integrity:
+            old_len = len(self._axes[axis])
+            new_len = len(new_labels)
 
-        if new_len != old_len:
-            raise ValueError(
-                f"Length mismatch: Expected axis has {old_len} elements, new "
-                f"values have {new_len} elements"
-            )
+            if new_len != old_len:
+                raise ValueError(
+                    f"Length mismatch: Expected axis has {old_len} elements, new "
+                    f"values have {new_len} elements"
+                )
 
         self._axes[axis] = new_labels
 
@@ -254,6 +256,30 @@ def reduce(
         new_mgr = type(self)(result_arrays, [index, columns])
         return new_mgr, indexer
 
+    def grouped_reduce(self: T, func: Callable, ignore_failures: bool = False) -> T:
+        """
+        Apply grouped reduction function columnwise, returning a new ArrayManager.
+
+        Parameters
+        ----------
+        func : grouped reduction function
+        ignore_failures : bool, default False
+            Whether to drop columns where func raises TypeError.
+
+        Returns
+        -------
+        ArrayManager
+        """
+        # TODO ignore_failures
+        result_arrays = [func(arr) for arr in self.arrays]
+
+        if len(result_arrays) == 0:
+            index = Index([None])  # placeholder
+        else:
+            index = Index(range(result_arrays[0].shape[0]))
+
+        return type(self)(result_arrays, [index, self.items])
+
     def operate_blockwise(self, other: ArrayManager, array_op) -> ArrayManager:
         """
         Apply array_op blockwise with another (aligned) BlockManager.
@@ -369,7 +395,7 @@ def apply_with_block(self: T, f, align_keys=None, **kwargs) -> T:
             if hasattr(arr, "tz") and arr.tz is None:  # type: ignore[union-attr]
                 # DatetimeArray needs to be converted to ndarray for DatetimeBlock
                 arr = arr._data  # type: ignore[union-attr]
-            elif arr.dtype.kind == "m":
+            elif arr.dtype.kind == "m" and not isinstance(arr, np.ndarray):
                 # TimedeltaArray needs to be converted to ndarray for TimedeltaBlock
                 arr = arr._data  # type: ignore[union-attr]
             if isinstance(arr, np.ndarray):

pandas/core/internals/managers.py

@@ -234,16 +234,19 @@ def shape(self) -> Shape:
     def ndim(self) -> int:
         return len(self.axes)
 
-    def set_axis(self, axis: int, new_labels: Index) -> None:
+    def set_axis(
+        self, axis: int, new_labels: Index, verify_integrity: bool = True
+    ) -> None:
         # Caller is responsible for ensuring we have an Index object.
-        old_len = len(self.axes[axis])
-        new_len = len(new_labels)
+        if verify_integrity:
+            old_len = len(self.axes[axis])
+            new_len = len(new_labels)
 
-        if new_len != old_len:
-            raise ValueError(
-                f"Length mismatch: Expected axis has {old_len} elements, new "
-                f"values have {new_len} elements"
-            )
+            if new_len != old_len:
+                raise ValueError(
+                    f"Length mismatch: Expected axis has {old_len} elements, new "
+                    f"values have {new_len} elements"
+                )
 
         self.axes[axis] = new_labels
 
@@ -282,16 +285,15 @@ def get_dtypes(self):
         return algos.take_nd(dtypes, self.blknos, allow_fill=False)
 
     @property
-    def arrays(self):
+    def arrays(self) -> List[ArrayLike]:
         """
         Quick access to the backing arrays of the Blocks.
 
         Only for compatibility with ArrayManager for testing convenience.
         Not to be used in actual code, and return value is not the same as the
         ArrayManager method (list of 1D arrays vs iterator of 2D ndarrays / 1D EAs).
         """
-        for blk in self.blocks:
-            yield blk.values
+        return [blk.values for blk in self.blocks]
 
     def __getstate__(self):
         block_values = [b.values for b in self.blocks]

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -10,6 +10,7 @@
 import pytest
 
 from pandas.errors import PerformanceWarning
+import pandas.util._test_decorators as td
 
 from pandas.core.dtypes.common import is_integer_dtype
 
@@ -45,6 +46,7 @@ def test_agg_regression1(tsframe):
     tm.assert_frame_equal(result, expected)
 
 
+@td.skip_array_manager_not_yet_implemented  # TODO(ArrayManager) quantile/describe
 def test_agg_must_agg(df):
     grouped = df.groupby("A")["C"]
 
@@ -134,6 +136,7 @@ def test_groupby_aggregation_multi_level_column():
     tm.assert_frame_equal(result, expected)
 
 
+@td.skip_array_manager_not_yet_implemented  # TODO(ArrayManager) non-cython agg
 def test_agg_apply_corner(ts, tsframe):
     # nothing to group, all NA
     grouped = ts.groupby(ts * np.nan)
@@ -212,6 +215,7 @@ def test_aggregate_str_func(tsframe, groupbyfunc):
     tm.assert_frame_equal(result, expected)
 
 
+@td.skip_array_manager_not_yet_implemented  # TODO(ArrayManager) non-cython agg
 def test_agg_str_with_kwarg_axis_1_raises(df, reduction_func):
     gb = df.groupby(level=0)
     if reduction_func in ("idxmax", "idxmin"):
@@ -491,6 +495,7 @@ def test_agg_index_has_complex_internals(index):
     tm.assert_frame_equal(result, expected)
 
 
+@td.skip_array_manager_not_yet_implemented  # TODO(ArrayManager) agg py_fallback
 def test_agg_split_block():
     # https://github.com/pandas-dev/pandas/issues/31522
     df = DataFrame(
@@ -508,6 +513,7 @@ def test_agg_split_block():
     tm.assert_frame_equal(result, expected)
 
 
+@td.skip_array_manager_not_yet_implemented  # TODO(ArrayManager) agg py_fallback
 def test_agg_split_object_part_datetime():
     # https://github.com/pandas-dev/pandas/pull/31616
     df = DataFrame(
@@ -1199,6 +1205,7 @@ def test_aggregate_datetime_objects():
     tm.assert_series_equal(result, expected)
 
 
+@td.skip_array_manager_not_yet_implemented  # TODO(ArrayManager) agg py_fallback
 def test_aggregate_numeric_object_dtype():
     # https://github.com/pandas-dev/pandas/issues/39329
     # simplified case: multiple object columns where one is all-NaN

pandas/tests/groupby/aggregate/test_cython.py

@@ -281,7 +281,7 @@ def test_read_only_buffer_source_agg(agg):
             "species": ["setosa", "setosa", "setosa", "setosa", "setosa"],
         }
     )
-    df._mgr.blocks[0].values.flags.writeable = False
+    df._mgr.arrays[0].flags.writeable = False
 
     result = df.groupby(["species"]).agg({"sepal_length": agg})
     expected = df.copy().groupby(["species"]).agg({"sepal_length": agg})

pandas/tests/groupby/aggregate/test_other.py

@@ -8,6 +8,8 @@
 import numpy as np
 import pytest
 
+import pandas.util._test_decorators as td
+
 import pandas as pd
 from pandas import (
     DataFrame,
@@ -412,6 +414,7 @@ def __call__(self, x):
         tm.assert_frame_equal(result, expected)
 
 
+@td.skip_array_manager_not_yet_implemented  # TODO(ArrayManager) columns with ndarrays
 def test_agg_over_numpy_arrays():
     # GH 3788
     df = DataFrame(