API: dont infer dtype for object-dtype groupby reductions (#51205)

* API: dont infer dtype for object-dtype groupby reductions

* GH ref

Author: jbrockmendel

doc/source/whatsnew/v2.0.0.rst

@@ -790,7 +790,9 @@ Other API changes
 - The levels of the index of the :class:`Series` returned from ``Series.sparse.from_coo`` now always have dtype ``int32``. Previously they had dtype ``int64`` (:issue:`50926`)
 - :func:`to_datetime` with ``unit`` of either "Y" or "M" will now raise if a sequence contains a non-round ``float`` value, matching the ``Timestamp`` behavior (:issue:`50301`)
 - The methods :meth:`Series.round`, :meth:`DataFrame.__invert__`, :meth:`Series.__invert__`, :meth:`DataFrame.swapaxes`, :meth:`DataFrame.first`, :meth:`DataFrame.last`, :meth:`Series.first`, :meth:`Series.last` and :meth:`DataFrame.align` will now always return new objects (:issue:`51032`)
+- :class:`DataFrameGroupBy` aggregations (e.g. "sum") with object-dtype columns no longer infer non-object dtypes for their results, explicitly call ``result.infer_objects(copy=False)`` on the result to obtain the old behavior (:issue:`51205`)
 - Added :func:`pandas.api.types.is_any_real_numeric_dtype` to check for real numeric dtypes (:issue:`51152`)
+-
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_200.deprecations:

pandas/core/groupby/groupby.py

@@ -1495,6 +1495,9 @@ def _agg_py_fallback(
             # TODO: if we ever get "rank" working, exclude it here.
             res_values = type(values)._from_sequence(res_values, dtype=values.dtype)
 
+        elif ser.dtype == object:
+            res_values = res_values.astype(object, copy=False)
+
         # If we are DataFrameGroupBy and went through a SeriesGroupByPath
         # then we need to reshape
         # GH#32223 includes case with IntegerArray values, ndarray res_values
@@ -1537,8 +1540,7 @@ def array_func(values: ArrayLike) -> ArrayLike:
         new_mgr = data.grouped_reduce(array_func)
         res = self._wrap_agged_manager(new_mgr)
         out = self._wrap_aggregated_output(res)
-        if data.ndim == 2:
-            # TODO: don't special-case DataFrame vs Series
+        if self.axis == 1:
             out = out.infer_objects(copy=False)
         return out
 

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -258,6 +258,7 @@ def test_multiindex_groupby_mixed_cols_axis1(func, expected, dtype, result_dtype
     expected = DataFrame([expected] * 3, columns=["i", "j", "k"]).astype(
         result_dtype_dict
     )
+
     tm.assert_frame_equal(result, expected)
 
 
@@ -675,6 +676,7 @@ def test_agg_split_object_part_datetime():
             "F": [1],
         },
         index=np.array([0]),
+        dtype=object,
     )
     tm.assert_frame_equal(result, expected)
 

pandas/tests/groupby/aggregate/test_other.py

@@ -517,6 +517,7 @@ def test_sum_uint64_overflow():
     expected = DataFrame(
         {1: [9223372036854775809, 9223372036854775811, 9223372036854775813]},
         index=index,
+        dtype=object,
     )
 
     expected.index.name = 0

pandas/tests/groupby/test_function.py

@@ -1509,6 +1509,12 @@ def test_deprecate_numeric_only_series(dtype, groupby_func, request):
         "sum",
         "diff",
         "pct_change",
+        "var",
+        "mean",
+        "median",
+        "min",
+        "max",
+        "prod",
     )
 
     # Test default behavior; kernels that fail may be enabled in the future but kernels

pandas/tests/groupby/test_groupby.py

@@ -2380,7 +2380,9 @@ def test_groupby_duplicate_columns():
     ).astype(object)
     df.columns = ["A", "B", "B"]
     result = df.groupby([0, 0, 0, 0]).min()
-    expected = DataFrame([["e", "a", 1]], index=np.array([0]), columns=["A", "B", "B"])
+    expected = DataFrame(
+        [["e", "a", 1]], index=np.array([0]), columns=["A", "B", "B"], dtype=object
+    )
     tm.assert_frame_equal(result, expected)
 
 

pandas/tests/groupby/test_min_max.py

@@ -148,19 +148,25 @@ def test_aggregate_numeric_object_dtype():
         {"key": ["A", "A", "B", "B"], "col1": list("abcd"), "col2": [np.nan] * 4},
     ).astype(object)
     result = df.groupby("key").min()
-    expected = DataFrame(
-        {"key": ["A", "B"], "col1": ["a", "c"], "col2": [np.nan, np.nan]}
-    ).set_index("key")
+    expected = (
+        DataFrame(
+            {"key": ["A", "B"], "col1": ["a", "c"], "col2": [np.nan, np.nan]},
+        )
+        .set_index("key")
+        .astype(object)
+    )
     tm.assert_frame_equal(result, expected)
 
     # same but with numbers
     df = DataFrame(
         {"key": ["A", "A", "B", "B"], "col1": list("abcd"), "col2": range(4)},
     ).astype(object)
     result = df.groupby("key").min()
-    expected = DataFrame(
-        {"key": ["A", "B"], "col1": ["a", "c"], "col2": [0, 2]}
-    ).set_index("key")
+    expected = (
+        DataFrame({"key": ["A", "B"], "col1": ["a", "c"], "col2": [0, 2]})
+        .set_index("key")
+        .astype(object)
+    )
     tm.assert_frame_equal(result, expected)