Bug: GroupBy raising error with None in first level of MultiIndex

doc/source/whatsnew/v1.5.0.rst

@@ -917,6 +917,7 @@ Groupby/resample/rolling
 - Bug in :meth:`DataFrameGroupby.cumsum` with ``skipna=False`` giving incorrect results (:issue:`46216`)
 - Bug in :meth:`.GroupBy.cumsum` with ``timedelta64[ns]`` dtype failing to recognize ``NaT`` as a null value (:issue:`46216`)
 - Bug in :meth:`GroupBy.cummin` and :meth:`GroupBy.cummax` with nullable dtypes incorrectly altering the original data in place (:issue:`46220`)
+- Bug in :meth:`DataFrame.GroupBy` raising error when ``None`` is in first level of :class:`MultiIndex` (:issue:`47348`)
 - Bug in :meth:`GroupBy.cummax` with ``int64`` dtype with leading value being the smallest possible int64 (:issue:`46382`)
 - Bug in :meth:`GroupBy.max` with empty groups and ``uint64`` dtype incorrectly raising ``RuntimeError`` (:issue:`46408`)
 - Bug in :meth:`.GroupBy.apply` would fail when ``func`` was a string and args or kwargs were supplied (:issue:`46479`)

pandas/core/groupby/grouper.py

@@ -835,7 +835,11 @@ def get_grouper(
 
     # if the actual grouper should be obj[key]
     def is_in_axis(key) -> bool:
+
         if not _is_label_like(key):
+            if obj.ndim == 1:
+                return False
+
             # items -> .columns for DataFrame, .index for Series
             items = obj.axes[-1]
             try:

pandas/tests/groupby/test_groupby.py

@@ -2776,3 +2776,22 @@ def test_by_column_values_with_same_starting_value():
     ).set_index("Name")
 
     tm.assert_frame_equal(result, expected_result)
+
+
+def test_groupby_none_in_first_mi_level():
+    # GH#47348
+    arr = [[None, 1, 0, 1], [2, 3, 2, 3]]
+    ser = Series(1, index=MultiIndex.from_arrays(arr, names=["a", "b"]))
+    result = ser.groupby(level=[0, 1]).sum()
+    expected = Series(
+        [1, 2], MultiIndex.from_tuples([(0.0, 2), (1.0, 3)], names=["a", "b"])
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_none_column_name():
+    # GH#47348
+    df = DataFrame({None: [1, 1, 2, 2], "b": [1, 1, 2, 3], "c": [4, 5, 6, 7]})
+    result = df.groupby(by=[None]).sum()
+    expected = DataFrame({"b": [2, 5], "c": [9, 13]}, index=Index([1, 2], name=None))
+    tm.assert_frame_equal(result, expected)