DEPR: Enforce deprecation of dropping columns when numeric_only=False in groupby / resample

doc/source/whatsnew/v2.0.0.rst

@@ -557,6 +557,7 @@ Removal of prior version deprecations/changes
 - Enforced deprecation ``numeric_only=None`` (the default) in DataFrame reductions that would silently drop columns that raised; ``numeric_only`` now defaults to ``False`` (:issue:`41480`)
 - 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`)
 -
 
 .. ---------------------------------------------------------------------------

pandas/core/groupby/generic.py

@@ -1357,7 +1357,13 @@ def arr_func(bvalues: ArrayLike) -> ArrayLike:
 
         # We could use `mgr.apply` here and not have to set_axis, but
         #  we would have to do shape gymnastics for ArrayManager compat
-        res_mgr = mgr.grouped_reduce(arr_func, ignore_failures=True)
+        try:
+            res_mgr = mgr.grouped_reduce(
+                arr_func, ignore_failures=numeric_only is lib.no_default
+            )
+        except NotImplementedError as err:
+            # For NotImplementedError, args[0] is the error message
+            raise TypeError(err.args[0]) from err
         res_mgr.set_axis(1, mgr.axes[1])
 
         if len(res_mgr) < orig_mgr_len:

pandas/core/groupby/groupby.py

@@ -1655,6 +1655,7 @@ def _agg_general(
                 alt=npfunc,
                 numeric_only=numeric_only,
                 min_count=min_count,
+                ignore_failures=numeric_only is lib.no_default,
             )
             return result.__finalize__(self.obj, method="groupby")
 
@@ -2123,6 +2124,7 @@ def mean(
                 "mean",
                 alt=lambda x: Series(x).mean(numeric_only=numeric_only_bool),
                 numeric_only=numeric_only,
+                ignore_failures=numeric_only is lib.no_default,
             )
             return result.__finalize__(self.obj, method="groupby")
 
@@ -2152,6 +2154,7 @@ def median(self, numeric_only: bool | lib.NoDefault = lib.no_default):
             "median",
             alt=lambda x: Series(x).median(numeric_only=numeric_only_bool),
             numeric_only=numeric_only,
+            ignore_failures=numeric_only is lib.no_default,
         )
         return result.__finalize__(self.obj, method="groupby")
 
@@ -3756,7 +3759,9 @@ def blk_func(values: ArrayLike) -> ArrayLike:
         if numeric_only_bool:
             mgr = mgr.get_numeric_data()
 
-        res_mgr = mgr.grouped_reduce(blk_func, ignore_failures=True)
+        res_mgr = mgr.grouped_reduce(
+            blk_func, ignore_failures=numeric_only is lib.no_default
+        )
 
         if not is_ser and len(res_mgr.items) != orig_mgr_len:
             howstr = how.replace("group_", "")

pandas/tests/groupby/test_function.py

@@ -163,14 +163,12 @@ def test_averages(self, df, method):
                 "int",
                 "float",
                 "category_int",
-                "datetime",
-                "datetimetz",
-                "timedelta",
             ],
         )
 
-        with tm.assert_produces_warning(FutureWarning, match="Dropping invalid"):
-            result = getattr(gb, method)(numeric_only=False)
+        with pytest.raises(TypeError, match="[Cc]ould not convert"):
+            getattr(gb, method)(numeric_only=False)
+        result = getattr(gb, method)()
         tm.assert_frame_equal(result.reindex_like(expected), expected)
 
         expected_columns = expected.columns
@@ -252,30 +250,35 @@ def test_cummin_cummax(self, df, method):
     def _check(self, df, method, expected_columns, expected_columns_numeric):
         gb = df.groupby("group")
 
-        # cummin, cummax dont have numeric_only kwarg, always use False
-        warn = None
-        if method in ["cummin", "cummax"]:
-            # these dont have numeric_only kwarg, always use False
-            warn = FutureWarning
-        elif method in ["min", "max"]:
-            # these have numeric_only kwarg, but default to False
-            warn = FutureWarning
-
-        with tm.assert_produces_warning(
-            warn, match="Dropping invalid columns", raise_on_extra_warnings=False
-        ):
+        if method in ("min", "max", "cummin", "cummax"):
+            # The methods default to numeric_only=False and raise TypeError
+            msg = "|".join(
+                [
+                    "Categorical is not ordered",
+                    "function is not implemented for this dtype",
+                ]
+            )
+            with pytest.raises(TypeError, match=msg):
+                getattr(gb, method)()
+        else:
             result = getattr(gb, method)()
-
-        tm.assert_index_equal(result.columns, expected_columns_numeric)
-
-        # GH#41475 deprecated silently ignoring nuisance columns
-        warn = None
-        if len(expected_columns) < len(gb._obj_with_exclusions.columns):
-            warn = FutureWarning
-        with tm.assert_produces_warning(warn, match="Dropping invalid columns"):
+            tm.assert_index_equal(result.columns, expected_columns_numeric)
+
+        if method not in ("first", "last"):
+            msg = "|".join(
+                [
+                    "[Cc]ould not convert",
+                    "Categorical is not ordered",
+                    "category type does not support",
+                    "can't multiply sequence",
+                    "function is not implemented for this dtype",
+                ]
+            )
+            with pytest.raises(TypeError, match=msg):
+                getattr(gb, method)(numeric_only=False)
+        else:
             result = getattr(gb, method)(numeric_only=False)
-
-        tm.assert_index_equal(result.columns, expected_columns)
+            tm.assert_index_equal(result.columns, expected_columns)
 
 
 class TestGroupByNonCythonPaths:
@@ -1323,45 +1326,45 @@ def test_groupby_sum_timedelta_with_nat():
 
 
 @pytest.mark.parametrize(
-    "kernel, numeric_only_default, drops_nuisance, has_arg",
+    "kernel, numeric_only_default, has_arg",
     [
-        ("all", False, False, False),
-        ("any", False, False, False),
-        ("bfill", False, False, False),
-        ("corr", True, False, True),
-        ("corrwith", True, False, True),
-        ("cov", True, False, True),
-        ("cummax", False, True, True),
-        ("cummin", False, True, True),
-        ("cumprod", True, True, True),
-        ("cumsum", True, True, True),
-        ("diff", False, False, False),
-        ("ffill", False, False, False),
-        ("fillna", False, False, False),
-        ("first", False, False, True),
-        ("idxmax", True, False, True),
-        ("idxmin", True, False, True),
-        ("last", False, False, True),
-        ("max", False, True, True),
-        ("mean", True, True, True),
-        ("median", True, True, True),
-        ("min", False, True, True),
-        ("nth", False, False, False),
-        ("nunique", False, False, False),
-        ("pct_change", False, False, False),
-        ("prod", True, True, True),
-        ("quantile", True, False, True),
-        ("sem", True, True, True),
-        ("skew", True, False, True),
-        ("std", True, True, True),
-        ("sum", True, True, True),
-        ("var", True, False, True),
+        ("all", False, False),
+        ("any", False, False),
+        ("bfill", False, False),
+        ("corr", True, True),
+        ("corrwith", True, True),
+        ("cov", True, True),
+        ("cummax", False, True),
+        ("cummin", False, True),
+        ("cumprod", True, True),
+        ("cumsum", True, True),
+        ("diff", False, False),
+        ("ffill", False, False),
+        ("fillna", False, False),
+        ("first", False, True),
+        ("idxmax", True, True),
+        ("idxmin", True, True),
+        ("last", False, True),
+        ("max", False, True),
+        ("mean", True, True),
+        ("median", True, True),
+        ("min", False, True),
+        ("nth", False, False),
+        ("nunique", False, False),
+        ("pct_change", False, False),
+        ("prod", True, True),
+        ("quantile", True, True),
+        ("sem", True, True),
+        ("skew", True, True),
+        ("std", True, True),
+        ("sum", True, True),
+        ("var", True, True),
     ],
 )
 @pytest.mark.parametrize("numeric_only", [True, False, lib.no_default])
 @pytest.mark.parametrize("keys", [["a1"], ["a1", "a2"]])
 def test_deprecate_numeric_only(
-    kernel, numeric_only_default, drops_nuisance, has_arg, numeric_only, keys
+    kernel, numeric_only_default, has_arg, numeric_only, keys
 ):
     # GH#46072
     # drops_nuisance: Whether the op drops nuisance columns even when numeric_only=False
@@ -1380,10 +1383,9 @@ def test_deprecate_numeric_only(
             # Cases where b does not appear in the result
             numeric_only is True
             or (numeric_only is lib.no_default and numeric_only_default)
-            or drops_nuisance
         )
     ):
-        if numeric_only is True or (not numeric_only_default and not drops_nuisance):
+        if numeric_only is True or not numeric_only_default:
             warn = None
         else:
             warn = FutureWarning
@@ -1408,14 +1410,17 @@ def test_deprecate_numeric_only(
         assert "b" in result.columns
     elif has_arg or kernel in ("idxmax", "idxmin"):
         assert numeric_only is not True
-        assert not drops_nuisance
         # kernels that are successful on any dtype were above; this will fail
-        msg = (
-            "(not allowed for this dtype"
-            "|must be a string or a number"
-            "|cannot be performed against 'object' dtypes"
-            "|must be a string or a real number"
-            "|unsupported operand type)"
+        msg = "|".join(
+            [
+                "not allowed for this dtype",
+                "must be a string or a number",
+                "cannot be performed against 'object' dtypes",
+                "must be a string or a real number",
+                "unsupported operand type",
+                "not supported between instances of",
+                "function is not implemented for this dtype",
+            ]
         )
         with pytest.raises(TypeError, match=msg):
             method(*args, **kwargs)

pandas/tests/groupby/test_groupby.py

@@ -915,11 +915,13 @@ def test_omit_nuisance_agg(df, agg_function, numeric_only):
 
     grouped = df.groupby("A")
 
-    if agg_function in ("var", "std", "sem") and numeric_only is False:
+    no_drop_nuisance = ("var", "std", "sem", "mean", "prod", "median")
+    if agg_function in no_drop_nuisance and numeric_only is False:
         # Added numeric_only as part of GH#46560; these do not drop nuisance
         # columns when numeric_only is False
-        klass = TypeError if agg_function == "var" else ValueError
-        with pytest.raises(klass, match="could not convert string to float"):
+        klass = ValueError if agg_function in ("std", "sem") else TypeError
+        msg = "|".join(["[C|c]ould not convert", "can't multiply sequence"])
+        with pytest.raises(klass, match=msg):
             getattr(grouped, agg_function)(numeric_only=numeric_only)
     else:
         if numeric_only is lib.no_default:
@@ -2049,10 +2051,13 @@ def get_result():
             and isinstance(values, Categorical)
             and len(keys) == 1
         ):
+            if op in ("min", "max"):
+                with pytest.raises(TypeError, match="Categorical is not ordered"):
+                    get_result()
+                return
             # Categorical doesn't implement, so with numeric_only=True
             #  these are dropped and we get an empty DataFrame back
             result = get_result()
-            expected = df.set_index(keys)[[]]
 
             # with numeric_only=True, these are dropped, and we get
             # an empty DataFrame back

pandas/tests/groupby/test_min_max.py

@@ -48,15 +48,17 @@ def test_max_min_object_multiple_columns(using_array_manager):
 
     gb = df.groupby("A")
 
-    with tm.assert_produces_warning(FutureWarning, match="Dropping invalid"):
-        result = gb.max(numeric_only=False)
+    with pytest.raises(TypeError, match="not supported between instances"):
+        gb.max(numeric_only=False)
+    result = gb[["C"]].max()
     # "max" is valid for column "C" but not for "B"
     ei = Index([1, 2, 3], name="A")
     expected = DataFrame({"C": ["b", "d", "e"]}, index=ei)
     tm.assert_frame_equal(result, expected)
 
-    with tm.assert_produces_warning(FutureWarning, match="Dropping invalid"):
-        result = gb.min(numeric_only=False)
+    with pytest.raises(TypeError, match="not supported between instances"):
+        gb.max(numeric_only=False)
+    result = gb[["C"]].min()
     # "min" is valid for column "C" but not for "B"
     ei = Index([1, 2, 3], name="A")
     expected = DataFrame({"C": ["a", "c", "e"]}, index=ei)

pandas/tests/resample/test_resample_api.py

@@ -821,8 +821,8 @@ def test_end_and_end_day_origin(
         ("sum", False, {"cat": ["cat_1cat_2"], "num": [25]}),
         ("sum", lib.no_default, {"num": [25]}),
         ("prod", True, {"num": [100]}),
-        ("prod", False, {"num": [100]}),
-        ("prod", lib.no_default, {"num": [100]}),
+        ("prod", False, "can't multiply sequence"),
+        ("prod", lib.no_default, "can't multiply sequence"),
         ("min", True, {"num": [5]}),
         ("min", False, {"cat": ["cat_1"], "num": [5]}),
         ("min", lib.no_default, {"cat": ["cat_1"], "num": [5]}),
@@ -836,10 +836,10 @@ def test_end_and_end_day_origin(
         ("last", False, {"cat": ["cat_2"], "num": [20]}),
         ("last", lib.no_default, {"cat": ["cat_2"], "num": [20]}),
         ("mean", True, {"num": [12.5]}),
-        ("mean", False, {"num": [12.5]}),
+        ("mean", False, "Could not convert"),
         ("mean", lib.no_default, {"num": [12.5]}),
         ("median", True, {"num": [12.5]}),
-        ("median", False, {"num": [12.5]}),
+        ("median", False, "could not convert"),
         ("median", lib.no_default, {"num": [12.5]}),
         ("std", True, {"num": [10.606601717798213]}),
         ("std", False, "could not convert string to float"),
@@ -876,15 +876,14 @@ def test_frame_downsample_method(method, numeric_only, expected_data):
         msg = (
             f"default value of numeric_only in DataFrameGroupBy.{method} is deprecated"
         )
-    elif method in ("prod", "mean", "median") and numeric_only is not True:
-        warn = FutureWarning
-        msg = f"Dropping invalid columns in DataFrameGroupBy.{method} is deprecated"
     else:
         warn = None
         msg = ""
     with tm.assert_produces_warning(warn, match=msg):
         if isinstance(expected_data, str):
-            klass = TypeError if method == "var" else ValueError
+            klass = (
+                TypeError if method in ("var", "mean", "median", "prod") else ValueError
+            )
             with pytest.raises(klass, match=expected_data):
                 _ = func(**kwargs)
         else: