BUG: Groupy dropped nan groups from result when grouping over single column

doc/source/whatsnew/v1.2.0.rst

@@ -410,6 +410,7 @@ Groupby/resample/rolling
 - Bug in :meth:`Rolling.sum()` returned wrong values when dtypes where mixed between float and integer and axis was equal to one (:issue:`20649`, :issue:`35596`)
 - Bug in :meth:`Rolling.count` returned ``np.nan`` with :class:`pandas.api.indexers.FixedForwardWindowIndexer` as window, ``min_periods=0`` and only missing values in window (:issue:`35579`)
 - Bug where :class:`pandas.core.window.Rolling` produces incorrect window sizes when using a ``PeriodIndex`` (:issue:`34225`)
+- Bug in :meth:`DataFrame.groupby` dropped ``nan`` groups from result with ``dropna=False`` when grouping over a single column (:issue:`35646`, :issue:`35542`)
 
 Reshaping
 ^^^^^^^^^

pandas/_libs/lib.pyx

@@ -902,7 +902,8 @@ def indices_fast(ndarray index, const int64_t[:] labels, list keys,
                     val = keys[j][sorted_labels[j][i - 1]]
                     PyTuple_SET_ITEM(tup, j, val)
                     Py_INCREF(val)
-
+                if len(tup) == 1:
+                    tup = tup[0]
                 result[tup] = index[start:i]
             start = i
         cur = lab
@@ -912,6 +913,8 @@ def indices_fast(ndarray index, const int64_t[:] labels, list keys,
         val = keys[j][sorted_labels[j][n - 1]]
         PyTuple_SET_ITEM(tup, j, val)
         Py_INCREF(val)
+    if len(tup) == 1:
+        tup = tup[0]
     result[tup] = index[start:]
 
     return result

pandas/core/groupby/ops.py

@@ -217,12 +217,9 @@ def apply(self, f: F, data: FrameOrSeries, axis: int = 0):
     @cache_readonly
     def indices(self):
         """ dict {group name -> group indices} """
-        if len(self.groupings) == 1:
-            return self.groupings[0].indices
-        else:
-            codes_list = [ping.codes for ping in self.groupings]
-            keys = [ping.group_index for ping in self.groupings]
-            return get_indexer_dict(codes_list, keys)
+        codes_list = [ping.codes for ping in self.groupings]
+        keys = [ping.group_index for ping in self.groupings]
+        return get_indexer_dict(codes_list, keys)
 
     @property
     def codes(self) -> List[np.ndarray]:

pandas/core/sorting.py

@@ -527,6 +527,8 @@ def get_indexer_dict(label_list, keys):
     shape = [len(x) for x in keys]
 
     group_index = get_group_index(label_list, shape, sort=True, xnull=True)
+    if np.all(group_index == -1):
+        return {}
     ngroups = (
         ((group_index.size and group_index.max()) + 1)
         if is_int64_overflow_possible(shape)

pandas/tests/groupby/test_groupby.py

@@ -1297,6 +1297,13 @@ def test_groupby_nat_exclude():
             grouped.get_group(pd.NaT)
 
 
+def test_groupby_two_group_keys_all_nan():
+    # GH #36842: Grouping over two group keys shouldn't raise an error
+    df = pd.DataFrame({"a": [np.nan, np.nan], "b": [np.nan, np.nan], "c": [1, 2]})
+    result = df.groupby(["a", "b"]).indices
+    assert result == {}
+
+
 def test_groupby_2d_malformed():
     d = DataFrame(index=range(2))
     d["group"] = ["g1", "g2"]

pandas/tests/groupby/test_groupby_dropna.py

@@ -2,7 +2,7 @@
 import pytest
 
 import pandas as pd
-import pandas.testing as tm
+import pandas._testing as tm
 
 
 @pytest.mark.parametrize(
@@ -336,3 +336,27 @@ def test_groupby_apply_with_dropna_for_multi_index(dropna, data, selected_data,
 
     expected = pd.DataFrame(selected_data, index=mi)
     tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_nan_included():
+    # GH 35646, GH 35542
+    data = {"group": ["g1", np.nan, "g1", "g2", np.nan], "B": [0, 1, 2, 3, 4]}
+    df = pd.DataFrame(data)
+    grouped = df.groupby("group", dropna=False)
+    result = grouped.indices
+    dtype = "int64"
+    expected = {
+        "g1": np.array([0, 2], dtype=dtype),
+        "g2": np.array([3], dtype=dtype),
+        np.nan: np.array([1, 4], dtype=dtype),
+    }
+    for result_values, expected_values in zip(result.values(), expected.values()):
+        tm.assert_numpy_array_equal(result_values, expected_values)
+    assert np.isnan(list(result.keys())[2])
+    assert list(result.keys())[0:2] == ["g1", "g2"]
+
+    result = grouped.mean()
+    expected = pd.DataFrame(
+        {"B": [1.0, 3.0, 2.5]}, index=pd.Index(["g1", "g2", np.nan], name="group")
+    )
+    tm.assert_frame_equal(result, expected)