ENH: Numba groupby support multiple labels

doc/source/whatsnew/v2.1.0.rst

@@ -99,6 +99,7 @@ Other enhancements
 - :meth:`Categorical.from_codes` has gotten a ``validate`` parameter (:issue:`50975`)
 - :meth:`DataFrame.stack` gained the ``sort`` keyword to dictate whether the resulting :class:`MultiIndex` levels are sorted (:issue:`15105`)
 - :meth:`DataFrame.unstack` gained the ``sort`` keyword to dictate whether the resulting :class:`MultiIndex` levels are sorted (:issue:`15105`)
+- :meth:`DataFrameGroupby.agg` and :meth:`DataFrameGroupby.transform` now support grouping by multiple keys when the index is not a :class:`MultiIndex` for ``engine="numba"`` (:issue:`53486`)
 - :meth:`SeriesGroupby.agg` and :meth:`DataFrameGroupby.agg` now support passing in multiple functions for ``engine="numba"`` (:issue:`53486`)
 - Added ``engine_kwargs`` parameter to :meth:`DataFrame.to_excel` (:issue:`53220`)
 - Added a new parameter ``by_row`` to :meth:`Series.apply`. When set to ``False`` the supplied callables will always operate on the whole Series (:issue:`53400`).

pandas/core/groupby/groupby.py

@@ -1325,13 +1325,14 @@ def _numba_prep(self, data: DataFrame):
         sorted_ids = self.grouper._sorted_ids
 
         sorted_data = data.take(sorted_index, axis=self.axis).to_numpy()
-        if len(self.grouper.groupings) > 1:
-            raise NotImplementedError(
-                "More than 1 grouping labels are not supported with engine='numba'"
-            )
         # GH 46867
         index_data = data.index
         if isinstance(index_data, MultiIndex):
+            if len(self.grouper.groupings) > 1:
+                raise NotImplementedError(
+                    "Grouping with more than 1 grouping labels and "
+                    "a MultiIndex is not supported with engine='numba'"
+                )
             group_key = self.grouper.groupings[0].name
             index_data = index_data.get_level_values(group_key)
         sorted_index_data = index_data.take(sorted_index).to_numpy()

pandas/tests/groupby/aggregate/test_numba.py

@@ -339,7 +339,44 @@ def numba_func(values, index):
 
     df = DataFrame([{"A": 1, "B": 2, "C": 3}]).set_index(["A", "B"])
     engine_kwargs = {"nopython": nopython, "nogil": nogil, "parallel": parallel}
-    with pytest.raises(NotImplementedError, match="More than 1 grouping labels"):
+    with pytest.raises(NotImplementedError, match="more than 1 grouping labels"):
         df.groupby(["A", "B"]).agg(
             numba_func, engine="numba", engine_kwargs=engine_kwargs
         )
+
+
+@td.skip_if_no("numba")
+def test_multilabel_numba_vs_cython(numba_supported_reductions):
+    reduction, kwargs = numba_supported_reductions
+    df = DataFrame(
+        {
+            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
+            "B": ["one", "one", "two", "three", "two", "two", "one", "three"],
+            "C": np.random.randn(8),
+            "D": np.random.randn(8),
+        }
+    )
+    gb = df.groupby(["A", "B"])
+    res_agg = gb.agg(reduction, engine="numba", **kwargs)
+    expected_agg = gb.agg(reduction, engine="cython", **kwargs)
+    tm.assert_frame_equal(res_agg, expected_agg)
+    # Test that calling the aggregation directly also works
+    direct_res = getattr(gb, reduction)(engine="numba", **kwargs)
+    direct_expected = getattr(gb, reduction)(engine="cython", **kwargs)
+    tm.assert_frame_equal(direct_res, direct_expected)
+
+
+@td.skip_if_no("numba")
+def test_multilabel_udf_numba_vs_cython():
+    df = DataFrame(
+        {
+            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
+            "B": ["one", "one", "two", "three", "two", "two", "one", "three"],
+            "C": np.random.randn(8),
+            "D": np.random.randn(8),
+        }
+    )
+    gb = df.groupby(["A", "B"])
+    result = gb.agg(lambda values, index: values.min(), engine="numba")
+    expected = gb.agg(lambda x: x.min(), engine="cython")
+    tm.assert_frame_equal(result, expected)

pandas/tests/groupby/transform/test_numba.py

@@ -1,3 +1,4 @@
+import numpy as np
 import pytest
 
 from pandas.errors import NumbaUtilError
@@ -224,7 +225,48 @@ def numba_func(values, index):
 
     df = DataFrame([{"A": 1, "B": 2, "C": 3}]).set_index(["A", "B"])
     engine_kwargs = {"nopython": nopython, "nogil": nogil, "parallel": parallel}
-    with pytest.raises(NotImplementedError, match="More than 1 grouping labels"):
+    with pytest.raises(NotImplementedError, match="more than 1 grouping labels"):
         df.groupby(["A", "B"]).transform(
             numba_func, engine="numba", engine_kwargs=engine_kwargs
         )
+
+
+@td.skip_if_no("numba")
+@pytest.mark.xfail(
+    reason="Groupby transform doesn't support strings as function inputs yet with numba"
+)
+def test_multilabel_numba_vs_cython(numba_supported_reductions):
+    reduction, kwargs = numba_supported_reductions
+    df = DataFrame(
+        {
+            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
+            "B": ["one", "one", "two", "three", "two", "two", "one", "three"],
+            "C": np.random.randn(8),
+            "D": np.random.randn(8),
+        }
+    )
+    gb = df.groupby(["A", "B"])
+    res_agg = gb.transform(reduction, engine="numba", **kwargs)
+    expected_agg = gb.transform(reduction, engine="cython", **kwargs)
+    tm.assert_frame_equal(res_agg, expected_agg)
+
+
+@td.skip_if_no("numba")
+def test_multilabel_udf_numba_vs_cython():
+    df = DataFrame(
+        {
+            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
+            "B": ["one", "one", "two", "three", "two", "two", "one", "three"],
+            "C": np.random.randn(8),
+            "D": np.random.randn(8),
+        }
+    )
+    gb = df.groupby(["A", "B"])
+    result = gb.transform(
+        lambda values, index: (values - values.min()) / (values.max() - values.min()),
+        engine="numba",
+    )
+    expected = gb.transform(
+        lambda x: (x - x.min()) / (x.max() - x.min()), engine="cython"
+    )
+    tm.assert_frame_equal(result, expected)