BUG: groupby.count maintains masked and arrow dtypes

doc/source/whatsnew/v2.1.0.rst

@@ -569,6 +569,7 @@ Groupby/resample/rolling
 - 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`)
+- Bug in :meth:`SeriesGroupBy.count` and :meth:`DataFrameGroupBy.count` where the dtype would be ``np.int64`` for data with :class:`ArrowDtype` or masked dtypes (e.g. ``Int64``) (:issue:`53831`)
 - Bug in :meth:`SeriesGroupBy.nth` and :meth:`DataFrameGroupBy.nth` after performing column selection when using ``dropna="any"`` or ``dropna="all"`` would not subset columns (:issue:`53518`)
 - Bug in :meth:`SeriesGroupBy.nth` and :meth:`DataFrameGroupBy.nth` raised after performing column selection when using ``dropna="any"`` or ``dropna="all"`` resulted in rows being dropped (:issue:`53518`)
 - Bug in :meth:`SeriesGroupBy.sum` and :meth:`DataFrameGroupby.sum` summing ``np.inf + np.inf`` and ``(-np.inf) + (-np.inf)`` to ``np.nan`` (:issue:`53606`)

pandas/core/groupby/groupby.py

@@ -104,6 +104,7 @@ class providing the base-class of operations.
     Categorical,
     ExtensionArray,
     FloatingArray,
+    IntegerArray,
 )
 from pandas.core.base import (
     PandasObject,
@@ -2248,6 +2249,12 @@ def hfunc(bvalues: ArrayLike) -> ArrayLike:
                 masked = mask & ~isna(bvalues)
 
             counted = lib.count_level_2d(masked, labels=ids, max_bin=ngroups)
+            if isinstance(bvalues, BaseMaskedArray):
+                return IntegerArray(
+                    counted[0], mask=np.zeros(counted.shape[1], dtype=np.bool_)
+                )
+            elif isinstance(bvalues, ArrowExtensionArray):
+                return type(bvalues)._from_sequence(counted[0])
             if is_series:
                 assert counted.ndim == 2
                 assert counted.shape[0] == 1

pandas/tests/extension/test_arrow.py

@@ -3154,6 +3154,18 @@ def test_to_numpy_temporal(pa_type):
     tm.assert_numpy_array_equal(result, expected)
 
 
+def test_groupby_count_return_arrow_dtype(data_missing):
+    df = pd.DataFrame({"A": [1, 1], "B": data_missing, "C": data_missing})
+    result = df.groupby("A").count()
+    expected = pd.DataFrame(
+        [[1, 1]],
+        index=pd.Index([1], name="A"),
+        columns=["B", "C"],
+        dtype="int64[pyarrow]",
+    )
+    tm.assert_frame_equal(result, expected)
+
+
 def test_arrowextensiondtype_dataframe_repr():
     # GH 54062
     df = pd.DataFrame(

pandas/tests/groupby/aggregate/test_cython.py

@@ -343,9 +343,27 @@ def test_cython_agg_nullable_int(op_name):
         # the result is not yet consistently using Int64/Float64 dtype,
         # so for now just checking the values by casting to float
         result = result.astype("float64")
+    else:
+        result = result.astype("int64")
     tm.assert_series_equal(result, expected)
 
 
+@pytest.mark.parametrize("dtype", ["Int64", "Float64", "boolean"])
+def test_count_masked_returns_masked_dtype(dtype):
+    df = DataFrame(
+        {
+            "A": [1, 1],
+            "B": pd.array([1, pd.NA], dtype=dtype),
+            "C": pd.array([1, 1], dtype=dtype),
+        }
+    )
+    result = df.groupby("A").count()
+    expected = DataFrame(
+        [[1, 2]], index=Index([1], name="A"), columns=["B", "C"], dtype="Int64"
+    )
+    tm.assert_frame_equal(result, expected)
+
+
 @pytest.mark.parametrize("with_na", [True, False])
 @pytest.mark.parametrize(
     "op_name, action",