Fix error value_counts result with pyarrow categorical columns (#60949)

* BUG: Fix PyArrow array access in Categorical constructor for Index objects (#60563)

* TST: Add test for value_counts with Arrow dictionary dtype (#60563)

* DOC: Add changelog entry for PyArrow array access fix in Categorical (#60563)

* Update doc/source/whatsnew/v3.0.0.rst

Co-authored-by: Matthew Roeschke <[email protected]>

---------

Co-authored-by: Matthew Roeschke <[email protected]>

Author: chilin0525

doc/source/whatsnew/v3.0.0.rst

@@ -787,6 +787,7 @@ Sparse
 
 ExtensionArray
 ^^^^^^^^^^^^^^
+- Bug in :class:`Categorical` when constructing with an :class:`Index` with :class:`ArrowDtype` (:issue:`60563`)
 - Bug in :meth:`.arrays.ArrowExtensionArray.__setitem__` which caused wrong behavior when using an integer array with repeated values as a key (:issue:`58530`)
 - Bug in :meth:`api.types.is_datetime64_any_dtype` where a custom :class:`ExtensionDtype` would return ``False`` for array-likes (:issue:`57055`)
 - Bug in comparison between object with :class:`ArrowDtype` and incompatible-dtyped (e.g. string vs bool) incorrectly raising instead of returning all-``False`` (for ``==``) or all-``True`` (for ``!=``) (:issue:`59505`)

pandas/core/arrays/categorical.py

@@ -447,7 +447,12 @@ def __init__(
             if isinstance(values.dtype, ArrowDtype) and issubclass(
                 values.dtype.type, CategoricalDtypeType
             ):
-                arr = values._pa_array.combine_chunks()
+                from pandas import Index
+
+                if isinstance(values, Index):
+                    arr = values._data._pa_array.combine_chunks()
+                else:
+                    arr = values._pa_array.combine_chunks()
                 categories = arr.dictionary.to_pandas(types_mapper=ArrowDtype)
                 codes = arr.indices.to_numpy()
                 dtype = CategoricalDtype(categories, values.dtype.pyarrow_dtype.ordered)

pandas/tests/extension/test_arrow.py

@@ -3511,3 +3511,20 @@ def test_map_numeric_na_action():
     result = ser.map(lambda x: 42, na_action="ignore")
     expected = pd.Series([42.0, 42.0, np.nan], dtype="float64")
     tm.assert_series_equal(result, expected)
+
+
+def test_categorical_from_arrow_dictionary():
+    # GH 60563
+    df = pd.DataFrame(
+        {"A": ["a1", "a2"]}, dtype=ArrowDtype(pa.dictionary(pa.int32(), pa.utf8()))
+    )
+    result = df.value_counts(dropna=False)
+    expected = pd.Series(
+        [1, 1],
+        index=pd.MultiIndex.from_arrays(
+            [pd.Index(["a1", "a2"], dtype=ArrowDtype(pa.string()), name="A")]
+        ),
+        name="count",
+        dtype="int64",
+    )
+    tm.assert_series_equal(result, expected)