Fix convert_dtype issue.

Signed-off-by: Liang Yan <[email protected]>

Author: liang3zy22

pandas/core/dtypes/cast.py

@@ -463,27 +463,23 @@ def maybe_cast_pointwise_result(
     """
 
     if isinstance(dtype, ExtensionDtype):
-        if not isinstance(dtype, (CategoricalDtype, DatetimeTZDtype, ArrowDtype)):
+        if not isinstance(dtype, (CategoricalDtype, DatetimeTZDtype)):
             # TODO: avoid this special-casing
             # We have to special case categorical so as not to upcast
             # things like counts back to categorical
-
-            cls = dtype.construct_array_type()
-            if same_dtype:
-                result = _maybe_cast_to_extension_array(cls, result, dtype=dtype)
-            else:
-                result = _maybe_cast_to_extension_array(cls, result)
-        elif isinstance(dtype, ArrowDtype):
-            pyarrow_type = convert_dtypes(result, dtype_backend="pyarrow")
-            if isinstance(pyarrow_type, ExtensionDtype):
-                cls = pyarrow_type.construct_array_type()
-                result = _maybe_cast_to_extension_array(cls, result)
+            if isinstance(dtype, ArrowDtype):
+                pyarrow_type = convert_dtypes(result, dtype_backend="pyarrow")
             else:
+                pyarrow_type = np.dtype("object")
+            if not isinstance(pyarrow_type, ExtensionDtype):
                 cls = dtype.construct_array_type()
                 if same_dtype:
                     result = _maybe_cast_to_extension_array(cls, result, dtype=dtype)
                 else:
                     result = _maybe_cast_to_extension_array(cls, result)
+            else:
+                cls = pyarrow_type.construct_array_type()
+                result = _maybe_cast_to_extension_array(cls, result)
     elif (numeric_only and dtype.kind in "iufcb") or not numeric_only:
         result = maybe_downcast_to_dtype(result, dtype)
 

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -5,7 +5,6 @@
 import functools
 from functools import partial
 import re
-import typing
 
 import numpy as np
 import pytest
@@ -25,6 +24,7 @@
 )
 import pandas._testing as tm
 from pandas.core.groupby.grouper import Grouping
+from pandas.tests.arrays.string_.test_string_arrow import skip_if_no_pyarrow
 
 
 def test_groupby_agg_no_extra_calls():
@@ -1631,23 +1631,3 @@ def test_groupby_agg_extension_timedelta_cumsum_with_named_aggregation():
     )
     gb = df.groupby("grps")
     result = gb.agg(td=("td", "cumsum"))
-
-
-@skip_if_no_pyarrow
-def test_agg_arrow_type():
-    df = DataFrame.from_dict(
-        {
-            "category": ["A"] * 10 + ["B"] * 10,
-            "bool_numpy": [True] * 5 + [False] * 5 + [True] * 5 + [False] * 5,
-        }
-    )
-    df["bool_arrow"] = df["bool_numpy"].astype("bool[pyarrow]")
-    result = df.groupby("category").agg(lambda x: x.sum() / x.count())
-    expected = DataFrame(
-        {
-            "bool_numpy": [0.5, 0.5],
-            "bool_arrow": Series([0.5, 0.5]).astype("double[pyarrow]").values,
-        },
-        index=Index(["A", "B"], name="category"),
-    )
-    tm.assert_frame_equal(result, expected)