BUG/API: SeriesGroupBy reduction with numeric_only=True

doc/source/whatsnew/v1.3.0.rst

@@ -906,6 +906,7 @@ Groupby/resample/rolling
 - Bug in :meth:`DataFrameGroupBy.__getitem__` with non-unique columns incorrectly returning a malformed :class:`SeriesGroupBy` instead of :class:`DataFrameGroupBy` (:issue:`41427`)
 - Bug in :meth:`DataFrameGroupBy.transform` with non-unique columns incorrectly raising ``AttributeError`` (:issue:`41427`)
 - Bug in :meth:`Resampler.apply` with non-unique columns incorrectly dropping duplicated columns (:issue:`41445`)
+- Bug in :meth:`SeriesGroupBy` aggregations incorrectly returning empty :class:`Series` instead of raising ``TypeError`` on aggregations that are invalid for its dtype (:issue:`41342`)
 
 Reshaping
 ^^^^^^^^^

pandas/core/groupby/generic.py

@@ -323,15 +323,18 @@ def _aggregate_multiple_funcs(self, arg) -> DataFrame:
         return output
 
     def _cython_agg_general(
-        self, how: str, alt=None, numeric_only: bool = True, min_count: int = -1
+        self, how: str, alt: Callable, numeric_only: bool, min_count: int = -1
     ):
 
         obj = self._selected_obj
         objvals = obj._values
         data = obj._mgr
 
         if numeric_only and not is_numeric_dtype(obj.dtype):
-            raise DataError("No numeric types to aggregate")
+            # GH#41291 match Series behavior
+            raise NotImplementedError(
+                f"{type(self).__name__}.{how} does not implement numeric_only."
+            )
 
         # This is overkill because it is only called once, but is here to
         #  mirror the array_func used in DataFrameGroupBy._cython_agg_general
@@ -1063,7 +1066,7 @@ def _iterate_slices(self) -> Iterable[Series]:
                 yield values
 
     def _cython_agg_general(
-        self, how: str, alt=None, numeric_only: bool = True, min_count: int = -1
+        self, how: str, alt: Callable, numeric_only: bool, min_count: int = -1
     ) -> DataFrame:
         # Note: we never get here with how="ohlc"; that goes through SeriesGroupBy
 

pandas/core/groupby/groupby.py

@@ -1099,6 +1099,24 @@ def _wrap_transformed_output(self, output: Mapping[base.OutputKey, ArrayLike]):
     def _wrap_applied_output(self, data, keys, values, not_indexed_same: bool = False):
         raise AbstractMethodError(self)
 
+    def _resolve_numeric_only(self, numeric_only: bool | lib.NoDefault) -> bool:
+        """
+        For SeriesGroupBy we want the default to be False (to match Series behavior).
+        For DataFrameGroupBy we want it to be True (for backwards-compat).
+        """
+        # GH#41291
+        if numeric_only is lib.no_default:
+            # i.e. not explicitly passed by user
+            if self.obj.ndim == 2:
+                # i.e. DataFrameGroupBy
+                numeric_only = True
+            else:
+                numeric_only = False
+
+        # error: Incompatible return value type (got "Union[bool, NoDefault]",
+        # expected "bool")
+        return numeric_only  # type: ignore[return-value]
+
     # -----------------------------------------------------------------
     # numba
 
@@ -1288,6 +1306,7 @@ def _agg_general(
         alias: str,
         npfunc: Callable,
     ):
+
         with group_selection_context(self):
             # try a cython aggregation if we can
             result = None
@@ -1344,7 +1363,7 @@ def _agg_py_fallback(
         return ensure_block_shape(res_values, ndim=ndim)
 
     def _cython_agg_general(
-        self, how: str, alt=None, numeric_only: bool = True, min_count: int = -1
+        self, how: str, alt: Callable, numeric_only: bool, min_count: int = -1
     ):
         raise AbstractMethodError(self)
 
@@ -1564,7 +1583,7 @@ def count(self):
     @final
     @Substitution(name="groupby")
     @Substitution(see_also=_common_see_also)
-    def mean(self, numeric_only: bool = True):
+    def mean(self, numeric_only: bool | lib.NoDefault = lib.no_default):
         """
         Compute mean of groups, excluding missing values.
 
@@ -1612,6 +1631,8 @@ def mean(self, numeric_only: bool = True):
         2    4.0
         Name: B, dtype: float64
         """
+        numeric_only = self._resolve_numeric_only(numeric_only)
+
         result = self._cython_agg_general(
             "mean",
             alt=lambda x: Series(x).mean(numeric_only=numeric_only),
@@ -1622,7 +1643,7 @@ def mean(self, numeric_only: bool = True):
     @final
     @Substitution(name="groupby")
     @Appender(_common_see_also)
-    def median(self, numeric_only=True):
+    def median(self, numeric_only: bool | lib.NoDefault = lib.no_default):
         """
         Compute median of groups, excluding missing values.
 
@@ -1639,6 +1660,8 @@ def median(self, numeric_only=True):
         Series or DataFrame
             Median of values within each group.
         """
+        numeric_only = self._resolve_numeric_only(numeric_only)
+
         result = self._cython_agg_general(
             "median",
             alt=lambda x: Series(x).median(numeric_only=numeric_only),
@@ -1696,8 +1719,9 @@ def var(self, ddof: int = 1):
             Variance of values within each group.
         """
         if ddof == 1:
+            numeric_only = self._resolve_numeric_only(lib.no_default)
             return self._cython_agg_general(
-                "var", alt=lambda x: Series(x).var(ddof=ddof)
+                "var", alt=lambda x: Series(x).var(ddof=ddof), numeric_only=numeric_only
             )
         else:
             func = lambda x: x.var(ddof=ddof)
@@ -1762,7 +1786,10 @@ def size(self) -> FrameOrSeriesUnion:
 
     @final
     @doc(_groupby_agg_method_template, fname="sum", no=True, mc=0)
-    def sum(self, numeric_only: bool = True, min_count: int = 0):
+    def sum(
+        self, numeric_only: bool | lib.NoDefault = lib.no_default, min_count: int = 0
+    ):
+        numeric_only = self._resolve_numeric_only(numeric_only)
 
         # If we are grouping on categoricals we want unobserved categories to
         # return zero, rather than the default of NaN which the reindexing in
@@ -1779,7 +1806,11 @@ def sum(self, numeric_only: bool = True, min_count: int = 0):
 
     @final
     @doc(_groupby_agg_method_template, fname="prod", no=True, mc=0)
-    def prod(self, numeric_only: bool = True, min_count: int = 0):
+    def prod(
+        self, numeric_only: bool | lib.NoDefault = lib.no_default, min_count: int = 0
+    ):
+        numeric_only = self._resolve_numeric_only(numeric_only)
+
         return self._agg_general(
             numeric_only=numeric_only, min_count=min_count, alias="prod", npfunc=np.prod
         )
@@ -2708,7 +2739,7 @@ def _get_cythonized_result(
         how: str,
         cython_dtype: np.dtype,
         aggregate: bool = False,
-        numeric_only: bool = True,
+        numeric_only: bool | lib.NoDefault = lib.no_default,
         needs_counts: bool = False,
         needs_values: bool = False,
         needs_2d: bool = False,
@@ -2776,6 +2807,8 @@ def _get_cythonized_result(
         -------
         `Series` or `DataFrame`  with filled values
         """
+        numeric_only = self._resolve_numeric_only(numeric_only)
+
         if result_is_index and aggregate:
             raise ValueError("'result_is_index' and 'aggregate' cannot both be True!")
         if post_processing and not callable(post_processing):

pandas/tests/groupby/aggregate/test_cython.py

@@ -91,7 +91,10 @@ def test_cython_agg_nothing_to_agg():
     frame = DataFrame({"a": np.random.randint(0, 5, 50), "b": ["foo", "bar"] * 25})
     msg = "No numeric types to aggregate"
 
-    with pytest.raises(DataError, match=msg):
+    with pytest.raises(NotImplementedError, match="does not implement"):
+        frame.groupby("a")["b"].mean(numeric_only=True)
+
+    with pytest.raises(TypeError, match="Could not convert (foo|bar)*"):
         frame.groupby("a")["b"].mean()
 
     frame = DataFrame({"a": np.random.randint(0, 5, 50), "b": ["foo", "bar"] * 25})
@@ -107,9 +110,8 @@ def test_cython_agg_nothing_to_agg_with_dates():
             "dates": pd.date_range("now", periods=50, freq="T"),
         }
     )
-    msg = "No numeric types to aggregate"
-    with pytest.raises(DataError, match=msg):
-        frame.groupby("b").dates.mean()
+    with pytest.raises(NotImplementedError, match="does not implement"):
+        frame.groupby("b").dates.mean(numeric_only=True)
 
 
 def test_cython_agg_frame_columns():
@@ -170,7 +172,7 @@ def test__cython_agg_general(op, targop):
     df = DataFrame(np.random.randn(1000))
     labels = np.random.randint(0, 50, size=1000).astype(float)
 
-    result = df.groupby(labels)._cython_agg_general(op)
+    result = df.groupby(labels)._cython_agg_general(op, alt=None, numeric_only=True)
     expected = df.groupby(labels).agg(targop)
     tm.assert_frame_equal(result, expected)
 
@@ -192,7 +194,7 @@ def test_cython_agg_empty_buckets(op, targop, observed):
     # calling _cython_agg_general directly, instead of via the user API
     # which sets different values for min_count, so do that here.
     g = df.groupby(pd.cut(df[0], grps), observed=observed)
-    result = g._cython_agg_general(op)
+    result = g._cython_agg_general(op, alt=None, numeric_only=True)
 
     g = df.groupby(pd.cut(df[0], grps), observed=observed)
     expected = g.agg(lambda x: targop(x))
@@ -209,7 +211,7 @@ def test_cython_agg_empty_buckets_nanops(observed):
     grps = range(0, 25, 5)
     # add / sum
     result = df.groupby(pd.cut(df["a"], grps), observed=observed)._cython_agg_general(
-        "add"
+        "add", alt=None, numeric_only=True
     )
     intervals = pd.interval_range(0, 20, freq=5)
     expected = DataFrame(
@@ -223,7 +225,7 @@ def test_cython_agg_empty_buckets_nanops(observed):
 
     # prod
     result = df.groupby(pd.cut(df["a"], grps), observed=observed)._cython_agg_general(
-        "prod"
+        "prod", alt=None, numeric_only=True
     )
     expected = DataFrame(
         {"a": [1, 1, 1716, 1]},

pandas/tests/groupby/test_groupby.py

@@ -1761,7 +1761,15 @@ def test_empty_groupby(columns, keys, values, method, op, request):
     # GH8093 & GH26411
     override_dtype = None
 
-    if isinstance(values, Categorical) and len(keys) == 1 and method == "apply":
+    if (
+        isinstance(values, Categorical)
+        and not isinstance(columns, list)
+        and op in ["sum", "prod"]
+        and method != "apply"
+    ):
+        # handled below GH#41291
+        pass
+    elif isinstance(values, Categorical) and len(keys) == 1 and method == "apply":
         mark = pytest.mark.xfail(raises=TypeError, match="'str' object is not callable")
         request.node.add_marker(mark)
     elif (
@@ -1822,11 +1830,36 @@ def test_empty_groupby(columns, keys, values, method, op, request):
     df = df.iloc[:0]
 
     gb = df.groupby(keys)[columns]
-    if method == "attr":
-        result = getattr(gb, op)()
-    else:
-        result = getattr(gb, method)(op)
 
+    def get_result():
+        if method == "attr":
+            return getattr(gb, op)()
+        else:
+            return getattr(gb, method)(op)
+
+    if columns == "C":
+        # i.e. SeriesGroupBy
+        if op in ["prod", "sum"]:
+            # ops that require more than just ordered-ness
+            if method != "apply":
+                # FIXME: apply goes through different code path
+                if df.dtypes[0].kind == "M":
+                    # GH#41291
+                    # datetime64 -> prod and sum are invalid
+                    msg = "datetime64 type does not support"
+                    with pytest.raises(TypeError, match=msg):
+                        get_result()
+
+                    return
+                elif isinstance(values, Categorical):
+                    # GH#41291
+                    msg = "category type does not support"
+                    with pytest.raises(TypeError, match=msg):
+                        get_result()
+
+                    return
+
+    result = get_result()
     expected = df.set_index(keys)[columns]
     if override_dtype is not None:
         expected = expected.astype(override_dtype)

pandas/tests/resample/test_datetime_index.py

@@ -61,15 +61,15 @@ def test_custom_grouper(index):
     g.ohlc()  # doesn't use _cython_agg_general
     funcs = ["add", "mean", "prod", "min", "max", "var"]
     for f in funcs:
-        g._cython_agg_general(f)
+        g._cython_agg_general(f, alt=None, numeric_only=True)
 
     b = Grouper(freq=Minute(5), closed="right", label="right")
     g = s.groupby(b)
     # check all cython functions work
     g.ohlc()  # doesn't use _cython_agg_general
     funcs = ["add", "mean", "prod", "min", "max", "var"]
     for f in funcs:
-        g._cython_agg_general(f)
+        g._cython_agg_general(f, alt=None, numeric_only=True)
 
     assert g.ngroups == 2593
     assert notna(g.mean()).all()
@@ -417,7 +417,7 @@ def test_resample_frame_basic():
     # check all cython functions work
     funcs = ["add", "mean", "prod", "min", "max", "var"]
     for f in funcs:
-        g._cython_agg_general(f)
+        g._cython_agg_general(f, alt=None, numeric_only=True)
 
     result = df.resample("A").mean()
     tm.assert_series_equal(result["A"], df["A"].resample("A").mean())