BUG: ArrowExtensionArray.mode(dropna=False) not respecting NAs

doc/source/whatsnew/v2.0.0.rst

@@ -1004,6 +1004,7 @@ Numeric
 - Bug in DataFrame reduction methods (e.g. :meth:`DataFrame.sum`) with object dtype, ``axis=1`` and ``numeric_only=False`` would not be coerced to float (:issue:`49551`)
 - Bug in :meth:`DataFrame.sem` and :meth:`Series.sem` where an erroneous ``TypeError`` would always raise when using data backed by an :class:`ArrowDtype` (:issue:`49759`)
 - Bug in :meth:`Series.__add__` casting to object for list and masked :class:`Series` (:issue:`22962`)
+- Bug in :meth:`~arrays.ArrowExtensionArray.mode` where ``dropna=False`` was not respected when there was ``NA`` values (:issue:`50982`)
 
 Conversion
 ^^^^^^^^^^

pandas/core/arrays/arrow/array.py

@@ -1269,7 +1269,6 @@ def _mode(self: ArrowExtensionArrayT, dropna: bool = True) -> ArrowExtensionArra
         ----------
         dropna : bool, default True
             Don't consider counts of NA values.
-            Not implemented by pyarrow.
 
         Returns
         -------
@@ -1292,15 +1291,21 @@ def _mode(self: ArrowExtensionArrayT, dropna: bool = True) -> ArrowExtensionArra
             data = self._data
 
         modes = pc.mode(data, pc.count_distinct(data).as_py())
-        values = modes.field(0)
         counts = modes.field(1)
         # counts sorted descending i.e counts[0] = max
+        if not dropna and self._data.null_count > counts[0].as_py():
+            return type(self)(pa.array([None], type=pa_type))
         mask = pc.equal(counts, counts[0])
-        most_common = values.filter(mask)
+        most_common = modes.field(0).filter(mask)
 
         if pa.types.is_temporal(pa_type):
             most_common = most_common.cast(pa_type)
 
+        if not dropna and self._data.null_count == counts[0].as_py():
+            most_common = pa.concat_arrays(
+                [most_common, pa.array([None], type=pa_type)]
+            )
+
         return type(self)(most_common)
 
     def _maybe_convert_setitem_value(self, value):

pandas/tests/extension/test_arrow.py

@@ -1385,6 +1385,27 @@ def test_mode(data_for_grouping, dropna, take_idx, exp_idx, request):
     tm.assert_series_equal(result, expected)
 
 
+def test_mode_dropna_false_mode_na(data, request):
+    # GH 50982
+    pa_dtype = data.dtype.pyarrow_dtype
+    if pa.types.is_string(pa_dtype) or pa.types.is_binary(pa_dtype):
+        request.node.add_marker(
+            pytest.mark.xfail(
+                raises=pa.ArrowNotImplementedError,
+                reason=f"mode not supported by pyarrow for {pa_dtype}",
+            )
+        )
+    more_nans = pd.Series([None, None, data[0]], dtype=data.dtype)
+    result = more_nans.mode(dropna=False)
+    expected = pd.Series([None], dtype=data.dtype)
+    tm.assert_series_equal(result, expected)
+
+    equal_nans = pd.Series([None, data[0]], dtype=data.dtype)
+    result = equal_nans.mode(dropna=False)
+    expected = pd.Series([data[0], None], dtype=data.dtype)
+    tm.assert_series_equal(result, expected)
+
+
 def test_is_bool_dtype():
     # GH 22667
     data = ArrowExtensionArray(pa.array([True, False, True]))