BUG: Fix inconsistency of converting empty categorical with dtype_backend='pyarrow' (#61131)

* Handling the case where converting empty categorical to 'pyarrow' dtype_backend results in error. Since conversion of non-empty categorical returns categorical of 'numpy_nullable' dtype_backend, now, instead of raising an error, we ensure empty categorical is returned as well.

* additional revisions

* removing the change for input_array...

* reverting newline in Series.convert_dtypes and precising respective docs in whatsnew

* revised testing to resolve CI errors v2

* Finish the pr

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

Co-authored-by: Matthew Roeschke <10647082+mroeschke@users.noreply.github.com>

* paramterize test

* move condition

---------

Co-authored-by: veljanin <veljkojovanovic1991@gmail.com>
Co-authored-by: Matthew Roeschke <10647082+mroeschke@users.noreply.github.com>

Author: 3 people

doc/source/whatsnew/v3.0.0.rst

@@ -636,6 +636,7 @@ Bug fixes
 Categorical
 ^^^^^^^^^^^
 - Bug in :func:`Series.apply` where ``nan`` was ignored for :class:`CategoricalDtype` (:issue:`59938`)
+- Bug in :meth:`Series.convert_dtypes` with ``dtype_backend="pyarrow"`` where empty :class:`CategoricalDtype` :class:`Series` raised an error or got converted to ``null[pyarrow]`` (:issue:`59934`)
 -
 
 Datetimelike

pandas/core/dtypes/cast.py

@@ -1127,6 +1127,7 @@ def convert_dtypes(
             or (
                 inferred_dtype.kind not in "iufcb"
                 and not isinstance(inferred_dtype, StringDtype)
+                and not isinstance(inferred_dtype, CategoricalDtype)
             )
         ):
             if isinstance(inferred_dtype, PandasExtensionDtype) and not isinstance(

pandas/tests/frame/methods/test_convert_dtypes.py

@@ -3,6 +3,8 @@
 import numpy as np
 import pytest
 
+import pandas.util._test_decorators as td
+
 import pandas as pd
 import pandas._testing as tm
 
@@ -35,6 +37,19 @@ def test_convert_empty(self):
         empty_df = pd.DataFrame()
         tm.assert_frame_equal(empty_df, empty_df.convert_dtypes())
 
+    @td.skip_if_no("pyarrow")
+    def test_convert_empty_categorical_to_pyarrow(self):
+        # GH#59934
+        df = pd.DataFrame(
+            {
+                "A": pd.Categorical([None] * 5),
+                "B": pd.Categorical([None] * 5, categories=["B1", "B2"]),
+            }
+        )
+        converted = df.convert_dtypes(dtype_backend="pyarrow")
+        expected = df
+        tm.assert_frame_equal(converted, expected)
+
     def test_convert_dtypes_retain_column_names(self):
         # GH#41435
         df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})

pandas/tests/series/methods/test_convert_dtypes.py

@@ -4,6 +4,7 @@
 import pytest
 
 from pandas._libs import lib
+import pandas.util._test_decorators as td
 
 import pandas as pd
 import pandas._testing as tm
@@ -298,6 +299,15 @@ def test_convert_dtypes_pyarrow_null(self):
         expected = pd.Series([None, None], dtype=pd.ArrowDtype(pa.null()))
         tm.assert_series_equal(result, expected)
 
+    @td.skip_if_no("pyarrow")
+    @pytest.mark.parametrize("categories", [None, ["S1", "S2"]])
+    def test_convert_empty_categorical_to_pyarrow(self, categories):
+        # GH#59934
+        ser = pd.Series(pd.Categorical([None] * 5, categories=categories))
+        converted = ser.convert_dtypes(dtype_backend="pyarrow")
+        expected = ser
+        tm.assert_series_equal(converted, expected)
+
     def test_convert_dtype_pyarrow_timezone_preserve(self):
         # GH 60237
         pytest.importorskip("pyarrow")