BUG: Series.groupby.size returning int64 for masked and arrow types

doc/source/whatsnew/v2.1.0.rst

@@ -559,6 +559,7 @@ Groupby/resample/rolling
 - Bug in :meth:`DataFrameGroupBy.apply` raising a ``TypeError`` when selecting multiple columns and providing a function that returns ``np.ndarray`` results (:issue:`18930`)
 - Bug in :meth:`GroupBy.groups` with a datetime key in conjunction with another key produced incorrect number of group keys (:issue:`51158`)
 - Bug in :meth:`GroupBy.quantile` may implicitly sort the result index with ``sort=False`` (:issue:`53009`)
+- Bug in :meth:`SeriesGroupBy.size` where the dtype would be ``np.int64`` for data with :class:`ArrowDtype` or masked dtypes (e.g. ``Int64``) (:issue:`53831`)
 - Bug in :meth:`GroupBy.var` failing to raise ``TypeError`` when called with datetime64, timedelta64 or :class:`PeriodDtype` values (:issue:`52128`, :issue:`53045`)
 - Bug in :meth:`DataFrameGroupby.resample` with ``kind="period"`` raising ``AttributeError`` (:issue:`24103`)
 - Bug in :meth:`Resampler.ohlc` with empty object returning a :class:`Series` instead of empty :class:`DataFrame` (:issue:`42902`)

pandas/core/groupby/groupby.py

@@ -99,6 +99,7 @@ class providing the base-class of operations.
 from pandas.core._numba import executor
 from pandas.core.apply import warn_alias_replacement
 from pandas.core.arrays import (
+    ArrowExtensionArray,
     BaseMaskedArray,
     Categorical,
     ExtensionArray,
@@ -2930,13 +2931,29 @@ def size(self) -> DataFrame | Series:
         Freq: MS, dtype: int64
         """
         result = self.grouper.size()
+        dtype_backend: None | Literal["pyarrow", "numpy_nullable"] = None
+        if isinstance(self.obj, Series):
+            if isinstance(self.obj.array, ArrowExtensionArray):
+                dtype_backend = "pyarrow"
+            elif isinstance(self.obj.array, BaseMaskedArray):
+                dtype_backend = "numpy_nullable"
+        # TODO: For DataFrames what if columns are mixed arrow/numpy/masked?
 
         # GH28330 preserve subclassed Series/DataFrames through calls
         if isinstance(self.obj, Series):
             result = self._obj_1d_constructor(result, name=self.obj.name)
         else:
             result = self._obj_1d_constructor(result)
 
+        if dtype_backend is not None:
+            result = result.convert_dtypes(
+                infer_objects=False,
+                convert_string=False,
+                convert_boolean=False,
+                convert_floating=False,
+                dtype_backend=dtype_backend,
+            )
+
         with com.temp_setattr(self, "as_index", True):
             # size already has the desired behavior in GH#49519, but this makes the
             # as_index=False path of _reindex_output fail on categorical groupers.

pandas/tests/extension/test_arrow.py

@@ -3122,6 +3122,14 @@ def test_iter_temporal(pa_type):
     assert result == expected
 
 
+def test_groupby_series_size_returns_pa_int(data):
+    # GH 54132
+    ser = pd.Series(data[:3], index=["a", "a", "b"])
+    result = ser.groupby(level=0).size()
+    expected = pd.Series([2, 1], dtype="int64[pyarrow]", index=["a", "b"])
+    tm.assert_series_equal(result, expected)
+
+
 @pytest.mark.parametrize(
     "pa_type", tm.DATETIME_PYARROW_DTYPES + tm.TIMEDELTA_PYARROW_DTYPES
 )

pandas/tests/groupby/test_size.py

@@ -95,3 +95,12 @@ def test_size_on_categorical(as_index):
         expected = expected.set_index(["A", "B"])["size"].rename(None)
 
     tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["Int64", "Float64", "boolean"])
+def test_size_series_masked_type_returns_Int64(dtype):
+    # GH 54132
+    ser = Series([1, 1, 1], index=["a", "a", "b"], dtype=dtype)
+    result = ser.groupby(level=0).size()
+    expected = Series([2, 1], dtype="Int64", index=["a", "b"])
+    tm.assert_series_equal(result, expected)