ENH: Support for PyArrow JSON type in ArrowDtype.type

Improved extension type handling by using BaseExtensionType for consistent storage type resolution across all PyArrow extension types, including JSON.

Fixes #60958

Author: asharmalik19

doc/source/whatsnew/v3.0.0.rst

@@ -61,11 +61,11 @@ Other enhancements
 - :meth:`Series.cummin` and :meth:`Series.cummax` now supports :class:`CategoricalDtype` (:issue:`52335`)
 - :meth:`Series.plot` now correctly handle the ``ylabel`` parameter for pie charts, allowing for explicit control over the y-axis label (:issue:`58239`)
 - :meth:`DataFrame.plot.scatter` argument ``c`` now accepts a column of strings, where rows with the same string are colored identically (:issue:`16827` and :issue:`16485`)
+- :class:`ArrowDtype` now supports ``pyarrow.JsonType`` (:issue:`60958`)
 - :class:`DataFrameGroupBy` and :class:`SeriesGroupBy` methods ``sum``, ``mean``, ``median``, ``prod``, ``min``, ``max``, ``std``, ``var`` and ``sem`` now accept ``skipna`` parameter (:issue:`15675`)
 - :class:`Rolling` and :class:`Expanding` now support ``nunique`` (:issue:`26958`)
 - :class:`Rolling` and :class:`Expanding` now support aggregations ``first`` and ``last`` (:issue:`33155`)
 - :func:`read_parquet` accepts ``to_pandas_kwargs`` which are forwarded to :meth:`pyarrow.Table.to_pandas` which enables passing additional keywords to customize the conversion to pandas, such as ``maps_as_pydicts`` to read the Parquet map data type as python dictionaries (:issue:`56842`)
-- :meth: ``ArrowDtype.type`` now supports the pyarrow json data type (:issue:`60958`)
 - :meth:`.DataFrameGroupBy.transform`, :meth:`.SeriesGroupBy.transform`, :meth:`.DataFrameGroupBy.agg`, :meth:`.SeriesGroupBy.agg`, :meth:`.SeriesGroupBy.apply`, :meth:`.DataFrameGroupBy.apply` now support ``kurt`` (:issue:`40139`)
 - :meth:`DataFrameGroupBy.transform`, :meth:`SeriesGroupBy.transform`, :meth:`DataFrameGroupBy.agg`, :meth:`SeriesGroupBy.agg`, :meth:`RollingGroupby.apply`, :meth:`ExpandingGroupby.apply`, :meth:`Rolling.apply`, :meth:`Expanding.apply`, :meth:`DataFrame.apply` with ``engine="numba"`` now supports positional arguments passed as kwargs (:issue:`58995`)
 - :meth:`Rolling.agg`, :meth:`Expanding.agg` and :meth:`ExponentialMovingWindow.agg` now accept :class:`NamedAgg` aggregations through ``**kwargs`` (:issue:`28333`)

pandas/core/dtypes/dtypes.py

@@ -2265,10 +2265,8 @@ def type(self):
         elif pa.types.is_null(pa_type):
             # TODO: None? pd.NA? pa.null?
             return type(pa_type)
-        elif isinstance(pa_type, pa.ExtensionType):
+        elif isinstance(pa_type, pa.BaseExtensionType):
             return type(self)(pa_type.storage_type).type
-        elif isinstance(pa_type, pa.JsonType):
-            return str
         raise NotImplementedError(pa_type)
 
     @property

pandas/tests/extension/test_arrow.py

@@ -43,6 +43,7 @@
     pa_version_under13p0,
     pa_version_under14p0,
     pa_version_under20p0,
+    pa_version_under19p0,
 )
 
 from pandas.core.dtypes.dtypes import (
@@ -3555,6 +3556,9 @@ def test_categorical_from_arrow_dictionary():
     tm.assert_series_equal(result, expected)
 
 
+@pytest.mark.skipif(
+    pa_version_under19p0, reason="pa.json_ was introduced in pyarrow v19.0"
+)
 def test_arrow_json_type():
     # GH 60958
     dtype = ArrowDtype(pa.json_(pa.string()))