Backport PR pandas-dev#56672: BUG: dictionary type astype categorical using dictionary as categories

Author: phofl

doc/source/whatsnew/v2.2.0.rst

@@ -740,6 +740,7 @@ Categorical
 ^^^^^^^^^^^
 - :meth:`Categorical.isin` raising ``InvalidIndexError`` for categorical containing overlapping :class:`Interval` values (:issue:`34974`)
 - Bug in :meth:`CategoricalDtype.__eq__` returning ``False`` for unordered categorical data with mixed types (:issue:`55468`)
+- Bug when casting ``pa.dictionary`` to :class:`CategoricalDtype` using a ``pa.DictionaryArray`` as categories (:issue:`56672`)
 
 Datetimelike
 ^^^^^^^^^^^^

pandas/core/arrays/categorical.py

@@ -44,7 +44,9 @@
     pandas_dtype,
 )
 from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
     CategoricalDtype,
+    CategoricalDtypeType,
     ExtensionDtype,
 )
 from pandas.core.dtypes.generic import (
@@ -443,24 +445,32 @@ def __init__(
                 values = arr
 
         if dtype.categories is None:
-            if not isinstance(values, ABCIndex):
-                # in particular RangeIndex xref test_index_equal_range_categories
-                values = sanitize_array(values, None)
-            try:
-                codes, categories = factorize(values, sort=True)
-            except TypeError as err:
-                codes, categories = factorize(values, sort=False)
-                if dtype.ordered:
-                    # raise, as we don't have a sortable data structure and so
-                    # the user should give us one by specifying categories
-                    raise TypeError(
-                        "'values' is not ordered, please "
-                        "explicitly specify the categories order "
-                        "by passing in a categories argument."
-                    ) from err
-
-            # we're inferring from values
-            dtype = CategoricalDtype(categories, dtype.ordered)
+            if isinstance(values.dtype, ArrowDtype) and issubclass(
+                values.dtype.type, CategoricalDtypeType
+            ):
+                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)
+            else:
+                if not isinstance(values, ABCIndex):
+                    # in particular RangeIndex xref test_index_equal_range_categories
+                    values = sanitize_array(values, None)
+                try:
+                    codes, categories = factorize(values, sort=True)
+                except TypeError as err:
+                    codes, categories = factorize(values, sort=False)
+                    if dtype.ordered:
+                        # raise, as we don't have a sortable data structure and so
+                        # the user should give us one by specifying categories
+                        raise TypeError(
+                            "'values' is not ordered, please "
+                            "explicitly specify the categories order "
+                            "by passing in a categories argument."
+                        ) from err
+
+                # we're inferring from values
+                dtype = CategoricalDtype(categories, dtype.ordered)
 
         elif isinstance(values.dtype, CategoricalDtype):
             old_codes = extract_array(values)._codes

pandas/tests/extension/test_arrow.py

@@ -3234,6 +3234,22 @@ def test_factorize_chunked_dictionary():
     tm.assert_index_equal(res_uniques, exp_uniques)
 
 
+def test_dictionary_astype_categorical():
+    # GH#56672
+    arrs = [
+        pa.array(np.array(["a", "x", "c", "a"])).dictionary_encode(),
+        pa.array(np.array(["a", "d", "c"])).dictionary_encode(),
+    ]
+    ser = pd.Series(ArrowExtensionArray(pa.chunked_array(arrs)))
+    result = ser.astype("category")
+    categories = pd.Index(["a", "x", "c", "d"], dtype=ArrowDtype(pa.string()))
+    expected = pd.Series(
+        ["a", "x", "c", "a", "a", "d", "c"],
+        dtype=pd.CategoricalDtype(categories=categories),
+    )
+    tm.assert_series_equal(result, expected)
+
+
 def test_arrow_floordiv():
     # GH 55561
     a = pd.Series([-7], dtype="int64[pyarrow]")