REGR: Fixed AssertionError in groupby

doc/source/whatsnew/v1.0.1.rst

@@ -20,6 +20,7 @@ Fixed regressions
 - Fixed regression in ``DataFrame.__setitem__`` raising an ``AttributeError`` with a :class:`MultiIndex` and a non-monotonic indexer (:issue:`31449`)
 - Fixed regression in :class:`Series` multiplication when multiplying a numeric :class:`Series` with >10000 elements with a timedelta-like scalar (:issue:`31457`)
 - Fixed regression in :meth:`GroupBy.apply` if called with a function which returned a non-pandas non-scalar object (e.g. a list or numpy array) (:issue:`31441`)
+- Fixed regression in ``.groupby().agg()`` raising an ``AssertionError`` for some reductions like ``min`` on object-dtype columns (:issue:`31522`)
 - Fixed regression in :meth:`to_datetime` when parsing non-nanosecond resolution datetimes (:issue:`31491`)
 - Fixed regression in :meth:`~DataFrame.to_csv` where specifying an ``na_rep`` might truncate the values written (:issue:`31447`)
 - Fixed regression where setting :attr:`pd.options.display.max_colwidth` was not accepting negative integer. In addition, this behavior has been deprecated in favor of using ``None`` (:issue:`31532`)

pandas/core/groupby/generic.py

@@ -1022,6 +1022,10 @@ def _cython_agg_blocks(
         agg_blocks: List[Block] = []
         new_items: List[np.ndarray] = []
         deleted_items: List[np.ndarray] = []
+        # Some object-dtype blocks might be split into List[Block[T], Block[U]]
+        split_items: List[np.ndarray] = []
+        split_frames: List[DataFrame] = []
+
         no_result = object()
         for block in data.blocks:
             # Avoid inheriting result from earlier in the loop
@@ -1061,40 +1065,56 @@ def _cython_agg_blocks(
                 else:
                     result = cast(DataFrame, result)
                     # unwrap DataFrame to get array
+                    if len(result._data.blocks) != 1:
+                        # We've split an object block! Everything we've assumed
+                        # about a single block input returning a single block output
+                        # is a lie. To keep the code-path for the typical non-split case
+                        # clean, we choose to clean up this mess later on.
+                        split_items.append(locs)
+                        split_frames.append(result)
+                        continue
+
                     assert len(result._data.blocks) == 1
                     result = result._data.blocks[0].values
                     if isinstance(result, np.ndarray) and result.ndim == 1:
                         result = result.reshape(1, -1)
 
-            finally:
-                assert not isinstance(result, DataFrame)
-
-                if result is not no_result:
-                    # see if we can cast the block back to the original dtype
-                    result = maybe_downcast_numeric(result, block.dtype)
-
-                    if block.is_extension and isinstance(result, np.ndarray):
-                        # e.g. block.values was an IntegerArray
-                        # (1, N) case can occur if block.values was Categorical
-                        #  and result is ndarray[object]
-                        assert result.ndim == 1 or result.shape[0] == 1
-                        try:
-                            # Cast back if feasible
-                            result = type(block.values)._from_sequence(
-                                result.ravel(), dtype=block.values.dtype
-                            )
-                        except ValueError:
-                            # reshape to be valid for non-Extension Block
-                            result = result.reshape(1, -1)
+            assert not isinstance(result, DataFrame)
+
+            if result is not no_result:
+                # see if we can cast the block back to the original dtype
+                result = maybe_downcast_numeric(result, block.dtype)
+
+                if block.is_extension and isinstance(result, np.ndarray):
+                    # e.g. block.values was an IntegerArray
+                    # (1, N) case can occur if block.values was Categorical
+                    #  and result is ndarray[object]
+                    assert result.ndim == 1 or result.shape[0] == 1
+                    try:
+                        # Cast back if feasible
+                        result = type(block.values)._from_sequence(
+                            result.ravel(), dtype=block.values.dtype
+                        )
+                    except ValueError:
+                        # reshape to be valid for non-Extension Block
+                        result = result.reshape(1, -1)
 
-                    agg_block: Block = block.make_block(result)
+                agg_block: Block = block.make_block(result)
 
             new_items.append(locs)
             agg_blocks.append(agg_block)
 
-        if not agg_blocks:
+        if not (agg_blocks or split_frames):
             raise DataError("No numeric types to aggregate")
 
+        if split_items:
+            # Clean up the mess left over from split blocks.
+            for locs, result in zip(split_items, split_frames):
+                assert len(locs) == result.shape[1]
+                for i, loc in enumerate(locs):
+                    new_items.append(np.array([loc], dtype=locs.dtype))
+                    agg_blocks.append(result.iloc[:, [i]]._data.blocks[0])
+
         # reset the locs in the blocks to correspond to our
         # current ordering
         indexer = np.concatenate(new_items)

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -377,6 +377,49 @@ def test_agg_index_has_complex_internals(index):
     tm.assert_frame_equal(result, expected)
 
 
+def test_agg_split_block():
+    # https://github.com/pandas-dev/pandas/issues/31522
+    df = pd.DataFrame(
+        {
+            "key1": ["a", "a", "b", "b", "a"],
+            "key2": ["one", "two", "one", "two", "one"],
+            "key3": ["three", "three", "three", "six", "six"],
+        }
+    )
+    result = df.groupby("key1").min()
+    expected = pd.DataFrame(
+        {"key2": ["one", "one"], "key3": ["six", "six"]},
+        index=pd.Index(["a", "b"], name="key1"),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_agg_split_object_part_datetime():
+    # https://github.com/pandas-dev/pandas/pull/31616
+    df = pd.DataFrame(
+        {
+            "A": pd.date_range("2000", periods=4),
+            "B": ["a", "b", "c", "d"],
+            "C": [1, 2, 3, 4],
+            "D": ["b", "c", "d", "e"],
+            "E": pd.date_range("2000", periods=4),
+            "F": [1, 2, 3, 4],
+        }
+    ).astype(object)
+    result = df.groupby([0, 0, 0, 0]).min()
+    expected = pd.DataFrame(
+        {
+            "A": [pd.Timestamp("2000")],
+            "B": ["a"],
+            "C": [1],
+            "D": ["b"],
+            "E": [pd.Timestamp("2000")],
+            "F": [1],
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
 class TestNamedAggregationSeries:
     def test_series_named_agg(self):
         df = pd.Series([1, 2, 3, 4])