DEPR: Enforce numeric_only=False in groupby sum/mean (#49829)

* DEPR: Enforce numeric_only=False in groupby sum/mean

* cleanup

* Refinements

* whatsnew fixup

Author: rhshadrach

asv_bench/benchmarks/reshape.py

@@ -210,7 +210,7 @@ def time_pivot_table_categorical_observed(self):
         )
 
     def time_pivot_table_margins_only_column(self):
-        self.df.pivot_table(columns=["key2", "key3"], margins=True)
+        self.df.pivot_table(columns=["key1", "key2", "key3"], margins=True)
 
 
 class Crosstab:

doc/source/whatsnew/v2.0.0.rst

@@ -572,6 +572,7 @@ Removal of prior version deprecations/changes
 - Changed default of ``numeric_only`` to ``False`` in all DataFrame methods with that argument (:issue:`46096`, :issue:`46906`)
 - Changed default of ``numeric_only`` to ``False`` in :meth:`Series.rank` (:issue:`47561`)
 - Enforced deprecation of silently dropping nuisance columns in groupby and resample operations when ``numeric_only=False`` (:issue:`41475`)
+- Changed default of ``numeric_only`` to ``False`` in :meth:`.DataFrameGroupBy.sum` and :meth:`.DataFrameGroupBy.mean` (:issue:`46072`)
 -
 
 .. ---------------------------------------------------------------------------

pandas/core/groupby/groupby.py

@@ -2049,7 +2049,7 @@ def hfunc(bvalues: ArrayLike) -> ArrayLike:
     @Substitution(see_also=_common_see_also)
     def mean(
         self,
-        numeric_only: bool | lib.NoDefault = lib.no_default,
+        numeric_only: bool = False,
         engine: str = "cython",
         engine_kwargs: dict[str, bool] | None = None,
     ):
@@ -2058,12 +2058,12 @@ def mean(
 
         Parameters
         ----------
-        numeric_only : bool, default True
+        numeric_only : bool, default False
             Include only float, int, boolean columns.
 
             .. versionchanged:: 2.0.0
 
-                numeric_only no longer accepts ``None``.
+                numeric_only no longer accepts ``None`` and defaults to ``False``.
 
         engine : str, default None
             * ``'cython'`` : Runs the operation through C-extensions from cython.
@@ -2120,7 +2120,6 @@ def mean(
         2    4.0
         Name: B, dtype: float64
         """
-        numeric_only_bool = self._resolve_numeric_only("mean", numeric_only, axis=0)
 
         if maybe_use_numba(engine):
             from pandas.core._numba.kernels import sliding_mean
@@ -2129,7 +2128,7 @@ def mean(
         else:
             result = self._cython_agg_general(
                 "mean",
-                alt=lambda x: Series(x).mean(numeric_only=numeric_only_bool),
+                alt=lambda x: Series(x).mean(numeric_only=numeric_only),
                 numeric_only=numeric_only,
             )
             return result.__finalize__(self.obj, method="groupby")
@@ -2382,10 +2381,10 @@ def size(self) -> DataFrame | Series:
         return self._reindex_output(result, fill_value=0)
 
     @final
-    @doc(_groupby_agg_method_template, fname="sum", no=True, mc=0)
+    @doc(_groupby_agg_method_template, fname="sum", no=False, mc=0)
     def sum(
         self,
-        numeric_only: bool | lib.NoDefault = lib.no_default,
+        numeric_only: bool = False,
         min_count: int = 0,
         engine: str | None = None,
         engine_kwargs: dict[str, bool] | None = None,

pandas/tests/extension/base/groupby.py

@@ -1,6 +1,12 @@
 import pytest
 
-from pandas.core.dtypes.common import is_numeric_dtype
+from pandas.core.dtypes.common import (
+    is_bool_dtype,
+    is_numeric_dtype,
+    is_object_dtype,
+    is_period_dtype,
+    is_string_dtype,
+)
 
 import pandas as pd
 import pandas._testing as tm
@@ -100,17 +106,19 @@ def test_in_numeric_groupby(self, data_for_grouping):
         )
 
         dtype = data_for_grouping.dtype
-        if is_numeric_dtype(dtype) or dtype.name == "decimal":
-            warn = None
-        else:
-            warn = FutureWarning
-        msg = "The default value of numeric_only"
-        with tm.assert_produces_warning(warn, match=msg):
-            result = df.groupby("A").sum().columns
-
-        if data_for_grouping.dtype._is_numeric:
+        if (
+            is_numeric_dtype(dtype)
+            or is_bool_dtype(dtype)
+            or dtype.name == "decimal"
+            or is_string_dtype(dtype)
+            or is_period_dtype(dtype)
+            or is_object_dtype(dtype)
+        ):
             expected = pd.Index(["B", "C"])
+            result = df.groupby("A").sum().columns
         else:
             expected = pd.Index(["C"])
-
+            with pytest.raises(TypeError, match="does not support"):
+                df.groupby("A").sum().columns
+            result = df.groupby("A").sum(numeric_only=True).columns
         tm.assert_index_equal(result, expected)

pandas/tests/generic/test_frame.py

@@ -71,9 +71,7 @@ def test_metadata_propagation_indiv_groupby(self):
                 "D": np.random.randn(8),
             }
         )
-        msg = "The default value of numeric_only"
-        with tm.assert_produces_warning(FutureWarning, match=msg):
-            result = df.groupby("A").sum()
+        result = df.groupby("A").sum()
         tm.assert_metadata_equivalent(df, result)
 
     def test_metadata_propagation_indiv_resample(self):

pandas/tests/groupby/test_categorical.py

@@ -103,7 +103,10 @@ def test_basic():  # TODO: split this test
     gb = df.groupby("A", observed=False)
     exp_idx = CategoricalIndex(["a", "b", "z"], name="A", ordered=True)
     expected = DataFrame({"values": Series([3, 7, 0], index=exp_idx)})
-    result = gb.sum()
+    msg = "category type does not support sum operations"
+    with pytest.raises(TypeError, match=msg):
+        gb.sum()
+    result = gb.sum(numeric_only=True)
     tm.assert_frame_equal(result, expected)
 
     # GH 8623
@@ -338,7 +341,9 @@ def test_observed(observed):
 
     gb = df.groupby(["A", "B"], observed=observed)
     exp_index = MultiIndex.from_arrays([cat1, cat2], names=["A", "B"])
-    expected = DataFrame({"values": [1, 2, 3, 4]}, index=exp_index)
+    expected = DataFrame(
+        {"values": [1, 2, 3, 4], "C": ["foo", "bar", "foo", "bar"]}, index=exp_index
+    )
     result = gb.sum()
     if not observed:
         expected = cartesian_product_for_groupers(

pandas/tests/groupby/test_function.py

@@ -166,9 +166,12 @@ def test_averages(self, df, method):
             ],
         )
 
-        with pytest.raises(TypeError, match="[Cc]ould not convert"):
-            getattr(gb, method)(numeric_only=False)
-        result = getattr(gb, method)()
+        if method == "mean":
+            with pytest.raises(TypeError, match="[Cc]ould not convert"):
+                getattr(gb, method)()
+            result = getattr(gb, method)(numeric_only=True)
+        else:
+            result = getattr(gb, method)()
         tm.assert_frame_equal(result.reindex_like(expected), expected)
 
         expected_columns = expected.columns
@@ -264,6 +267,15 @@ def _check(self, df, method, expected_columns, expected_columns_numeric):
             )
             with pytest.raises(exception, match=msg):
                 getattr(gb, method)()
+        elif method in ("sum", "mean"):
+            msg = "|".join(
+                [
+                    "category type does not support sum operations",
+                    "Could not convert",
+                ]
+            )
+            with pytest.raises(exception, match=msg):
+                getattr(gb, method)()
         else:
             result = getattr(gb, method)()
             tm.assert_index_equal(result.columns, expected_columns_numeric)
@@ -1375,7 +1387,7 @@ def test_groupby_sum_timedelta_with_nat():
         ("idxmin", True, True),
         ("last", False, True),
         ("max", False, True),
-        ("mean", True, True),
+        ("mean", False, True),
         ("median", True, True),
         ("min", False, True),
         ("nth", False, False),
@@ -1386,7 +1398,7 @@ def test_groupby_sum_timedelta_with_nat():
         ("sem", True, True),
         ("skew", True, True),
         ("std", True, True),
-        ("sum", True, True),
+        ("sum", False, True),
         ("var", True, True),
     ],
 )