DEPR: Change default of numeric_only to False in Resampler methods (#49915)

* DEPR: Change default of numeric_only to False in Resampler methods

* more cleanup

Author: rhshadrach

doc/source/whatsnew/v2.0.0.rst

@@ -573,6 +573,7 @@ Removal of prior version deprecations/changes
 - 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`)
+- Changed default of ``numeric_only`` to ``False`` in :class:`.Resampler` methods (:issue:`47177`)
 -
 
 .. ---------------------------------------------------------------------------

pandas/core/resample.py

@@ -899,7 +899,7 @@ def asfreq(self, fill_value=None):
 
     def mean(
         self,
-        numeric_only: bool | lib.NoDefault = lib.no_default,
+        numeric_only: bool = False,
         *args,
         **kwargs,
     ):
@@ -911,6 +911,10 @@ def mean(
         numeric_only : bool, default False
             Include only `float`, `int` or `boolean` data.
 
+            .. versionchanged:: 2.0.0
+
+                numeric_only now defaults to ``False``.
+
         Returns
         -------
         DataFrame or Series
@@ -922,7 +926,7 @@ def mean(
     def std(
         self,
         ddof: int = 1,
-        numeric_only: bool | lib.NoDefault = lib.no_default,
+        numeric_only: bool = False,
         *args,
         **kwargs,
     ):
@@ -938,6 +942,10 @@ def std(
 
             .. versionadded:: 1.5.0
 
+            .. versionchanged:: 2.0.0
+
+                numeric_only now defaults to ``False``.
+
         Returns
         -------
         DataFrame or Series
@@ -949,7 +957,7 @@ def std(
     def var(
         self,
         ddof: int = 1,
-        numeric_only: bool | lib.NoDefault = lib.no_default,
+        numeric_only: bool = False,
         *args,
         **kwargs,
     ):
@@ -966,6 +974,10 @@ def var(
 
             .. versionadded:: 1.5.0
 
+            .. versionchanged:: 2.0.0
+
+                numeric_only now defaults to ``False``.
+
         Returns
         -------
         DataFrame or Series
@@ -1058,25 +1070,19 @@ def _add_downsample_kernel(
 
         def f(
             self,
-            numeric_only: bool | lib.NoDefault = lib.no_default,
+            numeric_only: bool = False,
             min_count: int = 0,
             *args,
             **kwargs,
         ):
             nv.validate_resampler_func(name, args, kwargs)
-            if numeric_only is lib.no_default and name != "sum":
-                # For DataFrameGroupBy, set it to be False for methods other than `sum`.
-                numeric_only = False
-
             return self._downsample(
                 name, numeric_only=numeric_only, min_count=min_count
             )
 
     elif args == ("numeric_only",):
         # error: All conditional function variants must have identical signatures
-        def f(  # type: ignore[misc]
-            self, numeric_only: bool | lib.NoDefault = lib.no_default, *args, **kwargs
-        ):
+        def f(self, numeric_only: bool = False, *args, **kwargs):  # type: ignore[misc]
             nv.validate_resampler_func(name, args, kwargs)
             return self._downsample(name, numeric_only=numeric_only)
 
@@ -1085,7 +1091,7 @@ def f(  # type: ignore[misc]
         def f(  # type: ignore[misc]
             self,
             ddof: int = 1,
-            numeric_only: bool | lib.NoDefault = lib.no_default,
+            numeric_only: bool = False,
             *args,
             **kwargs,
         ):

pandas/tests/frame/test_stack_unstack.py

@@ -1789,10 +1789,9 @@ def test_stack_multiple_bug(self):
         multi = df.set_index(["DATE", "ID"])
         multi.columns.name = "Params"
         unst = multi.unstack("ID")
-        msg = "The default value of numeric_only"
-        with tm.assert_produces_warning(FutureWarning, match=msg):
-            down = unst.resample("W-THU").mean()
-
+        with pytest.raises(TypeError, match="Could not convert"):
+            unst.resample("W-THU").mean()
+        down = unst.resample("W-THU").mean(numeric_only=True)
         rs = down.stack("ID")
         xp = unst.loc[:, ["VAR1"]].resample("W-THU").mean().stack("ID")
         xp.columns.name = "Params"

pandas/tests/groupby/test_groupby_subclass.py

@@ -101,7 +101,5 @@ def test_groupby_resample_preserves_subclass(obj):
     df = df.set_index("Date")
 
     # Confirm groupby.resample() preserves dataframe type
-    msg = "The default value of numeric_only"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        result = df.groupby("Buyer").resample("5D").sum()
+    result = df.groupby("Buyer").resample("5D").sum()
     assert isinstance(result, obj)

pandas/tests/groupby/test_timegrouper.py

@@ -108,10 +108,8 @@ def test_groupby_with_timegrouper(self):
             expected.iloc[18, 0] = "Joe"
             expected.iloc[[0, 6, 18], 1] = np.array([24, 6, 9], dtype="int64")
 
-            msg = "The default value of numeric_only"
-            with tm.assert_produces_warning(FutureWarning, match=msg):
-                result1 = df.resample("5D").sum()
-            tm.assert_frame_equal(result1, expected[["Quantity"]])
+            result1 = df.resample("5D").sum()
+            tm.assert_frame_equal(result1, expected)
 
             df_sorted = df.sort_index()
             result2 = df_sorted.groupby(Grouper(freq="5D")).sum()

pandas/tests/resample/test_resample_api.py

@@ -90,10 +90,8 @@ def test_groupby_resample_on_api():
         }
     )
 
-    msg = "The default value of numeric_only"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        expected = df.set_index("dates").groupby("key").resample("D").mean()
-        result = df.groupby("key").resample("D", on="dates").mean()
+    expected = df.set_index("dates").groupby("key").resample("D").mean()
+    result = df.groupby("key").resample("D", on="dates").mean()
     tm.assert_frame_equal(result, expected)
 
 
@@ -187,19 +185,19 @@ def test_api_compat_before_use(attr):
     getattr(rs, attr)
 
 
-def tests_skip_nuisance(test_frame):
+def tests_raises_on_nuisance(test_frame):
 
     df = test_frame
     df["D"] = "foo"
     r = df.resample("H")
-    result = r[["A", "B"]].sum()
-    expected = pd.concat([r.A.sum(), r.B.sum()], axis=1)
+    result = r[["A", "B"]].mean()
+    expected = pd.concat([r.A.mean(), r.B.mean()], axis=1)
     tm.assert_frame_equal(result, expected)
 
-    expected = r[["A", "B", "C"]].sum()
-    msg = "The default value of numeric_only"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        result = r.sum()
+    expected = r[["A", "B", "C"]].mean()
+    with pytest.raises(TypeError, match="Could not convert"):
+        r.mean()
+    result = r.mean(numeric_only=True)
     tm.assert_frame_equal(result, expected)
 
 
@@ -681,9 +679,9 @@ def test_selection_api_validation():
     tm.assert_frame_equal(exp, result)
 
     exp.index.name = "d"
-    msg = "The default value of numeric_only"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        result = df.resample("2D", level="d").sum()
+    with pytest.raises(TypeError, match="datetime64 type does not support sum"):
+        df.resample("2D", level="d").sum()
+    result = df.resample("2D", level="d").sum(numeric_only=True)
     tm.assert_frame_equal(exp, result)
 
 
@@ -819,7 +817,7 @@ def test_end_and_end_day_origin(
     [
         ("sum", True, {"num": [25]}),
         ("sum", False, {"cat": ["cat_1cat_2"], "num": [25]}),
-        ("sum", lib.no_default, {"num": [25]}),
+        ("sum", lib.no_default, {"cat": ["cat_1cat_2"], "num": [25]}),
         ("prod", True, {"num": [100]}),
         ("prod", False, "can't multiply sequence"),
         ("prod", lib.no_default, "can't multiply sequence"),
@@ -837,19 +835,19 @@ def test_end_and_end_day_origin(
         ("last", lib.no_default, {"cat": ["cat_2"], "num": [20]}),
         ("mean", True, {"num": [12.5]}),
         ("mean", False, "Could not convert"),
-        ("mean", lib.no_default, {"num": [12.5]}),
+        ("mean", lib.no_default, "Could not convert"),
         ("median", True, {"num": [12.5]}),
         ("median", False, "could not convert"),
-        ("median", lib.no_default, {"num": [12.5]}),
+        ("median", lib.no_default, "could not convert"),
         ("std", True, {"num": [10.606601717798213]}),
         ("std", False, "could not convert string to float"),
-        ("std", lib.no_default, {"num": [10.606601717798213]}),
+        ("std", lib.no_default, "could not convert string to float"),
         ("var", True, {"num": [112.5]}),
         ("var", False, "could not convert string to float"),
-        ("var", lib.no_default, {"num": [112.5]}),
+        ("var", lib.no_default, "could not convert string to float"),
         ("sem", True, {"num": [7.5]}),
         ("sem", False, "could not convert string to float"),
-        ("sem", lib.no_default, {"num": [7.5]}),
+        ("sem", lib.no_default, "could not convert string to float"),
     ],
 )
 def test_frame_downsample_method(method, numeric_only, expected_data):
@@ -865,31 +863,14 @@ def test_frame_downsample_method(method, numeric_only, expected_data):
         kwargs = {"numeric_only": numeric_only}
 
     func = getattr(resampled, method)
-    if numeric_only is lib.no_default and method not in (
-        "min",
-        "max",
-        "first",
-        "last",
-        "prod",
-    ):
-        warn = FutureWarning
-        msg = (
-            f"default value of numeric_only in DataFrameGroupBy.{method} is deprecated"
-        )
+    if isinstance(expected_data, str):
+        klass = TypeError if method in ("var", "mean", "median", "prod") else ValueError
+        with pytest.raises(klass, match=expected_data):
+            _ = func(**kwargs)
     else:
-        warn = None
-        msg = ""
-    with tm.assert_produces_warning(warn, match=msg):
-        if isinstance(expected_data, str):
-            klass = (
-                TypeError if method in ("var", "mean", "median", "prod") else ValueError
-            )
-            with pytest.raises(klass, match=expected_data):
-                _ = func(**kwargs)
-        else:
-            result = func(**kwargs)
-            expected = DataFrame(expected_data, index=expected_index)
-            tm.assert_frame_equal(result, expected)
+        result = func(**kwargs)
+        expected = DataFrame(expected_data, index=expected_index)
+        tm.assert_frame_equal(result, expected)
 
 
 @pytest.mark.parametrize(

pandas/tests/resample/test_resampler_grouper.py

@@ -408,9 +408,7 @@ def test_resample_groupby_agg():
     df["date"] = pd.to_datetime(df["date"])
 
     resampled = df.groupby("cat").resample("Y", on="date")
-    msg = "The default value of numeric_only"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        expected = resampled.sum()
+    expected = resampled[["num"]].sum()
     result = resampled.agg({"num": "sum"})
 
     tm.assert_frame_equal(result, expected)