Add tests for groupby dropna=False behavior

Add tests to ensure groupby with dropna=False preserves NaN groups in both DataFrame and Series.

Author: AnonToky

tests/groupby/test_groupby_dropna.py

@@ -0,0 +1,41 @@
+import numpy as np
+import pandas as pd
+import pandas._testing as tm
+
+def test_groupby_dataframe_dropna_false_preserves_nan_group():
+    # Ensure DataFrame.groupby(..., dropna=False) preserves NA entries as a single group
+    # Tests-only addition to lock current behavior (GHxxxx)
+    data = {"group": ["g1", np.nan, "g1", "g2", np.nan], "val": [0, 1, 2, 3, 4]}
+    df = pd.DataFrame(data)
+
+    gb_keepna = df.groupby("group", dropna=False)
+    result = gb_keepna.indices
+
+    # expected: g1 -> [0,2], g2 -> [3], NaN -> [1,4]
+    expected = {
+        "g1": np.array([0, 2], dtype=np.intp),
+        "g2": np.array([3], dtype=np.intp),
+        np.nan: np.array([1, 4], dtype=np.intp),
+    }
+
+    # Compare group indices allowing for np.nan key
+    for res_vals, exp_vals in zip(result.values(), expected.values()):
+        tm.assert_numpy_array_equal(res_vals, exp_vals)
+    # check there is an NaN key present
+    assert any(pd.isna(k) for k in result.keys())
+
+
+def test_groupby_series_dropna_false_preserves_nan_group():
+    # Verify Series.groupby(..., dropna=False) also preserves NA groups
+    s = pd.Series([1, 2, 3, 4], index=["a", np.nan, "a", np.nan], name="s")
+    gb = s.groupby(level=0, dropna=False)
+    res = gb.indices
+
+    expected = {
+        "a": np.array([0, 2], dtype=np.intp),
+        np.nan: np.array([1, 3], dtype=np.intp),
+    }
+
+    for res_vals, exp_vals in zip(res.values(), expected.values()):
+        tm.assert_numpy_array_equal(res_vals, exp_vals)
+    assert any(pd.isna(k) for k in res.keys())