BUG: groupby.filter handling of NA groups

pandas/core/groupby/groupby.py

@@ -642,6 +642,11 @@ def _get_indices(self, names):
         Safe get multiple indices, translate keys for
         datelike to underlying repr.
         """
+        assert len(names) == 1
+        if isna(names[0]):
+            return [self.indices.get(np.nan, [])]
+        if isinstance(names[0], tuple):
+            names[0] = tuple(np.nan if isna(comp) else comp for comp in names[0])
 
         def get_converter(s):
             # possibly convert to the actual key types

pandas/core/groupby/ops.py

@@ -652,9 +652,23 @@ def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]:
         """dict {group name -> group indices}"""
         if len(self.groupings) == 1 and isinstance(self.result_index, CategoricalIndex):
             # This shows unused categories in indices GH#38642
-            return self.groupings[0].indices
-        codes_list = [ping.codes for ping in self.groupings]
-        return get_indexer_dict(codes_list, self.levels)
+            result = self.groupings[0].indices
+        else:
+            codes_list = [ping.codes for ping in self.groupings]
+            result = get_indexer_dict(codes_list, self.levels)
+        if not self.dropna:
+            has_mi = isinstance(self.result_index, MultiIndex)
+            if not has_mi and self.result_index.hasnans:
+                result = {
+                    np.nan if isna(key) else key: value for key, value in result.items()
+                }
+            elif has_mi:
+                # MultiIndex has no efficient way to tell if there are NAs
+                result = {
+                    tuple(np.nan if isna(comp) else comp for comp in key): value
+                    for key, value in result.items()
+                }
+        return result
 
     @final
     @cache_readonly

pandas/tests/groupby/test_filters.py

@@ -606,3 +606,33 @@ def test_filter_consistent_result_before_after_agg_func():
     grouper.sum()
     result = grouper.filter(lambda x: True)
     tm.assert_frame_equal(result, expected)
+
+
+def test_filter_with_non_values():
+    # GH 62501
+    df = DataFrame(
+        [
+            [1],
+            [None],
+        ],
+        columns=["a"],
+    )
+
+    result = df.groupby("a", dropna=False).filter(lambda x: True)
+    tm.assert_frame_equal(result, df)
+
+
+def test_filter_with_non_values_multi_index():
+    # GH 62501
+    df = DataFrame(
+        [
+            [1, 2],
+            [3, None],
+            [None, 4],
+            [None, None],
+        ],
+        columns=["a", "b"],
+    )
+
+    result = df.groupby(["a", "b"], dropna=False).filter(lambda x: True)
+    tm.assert_frame_equal(result, df)