REGR: NumPy func warning when dropping nuisance in agg, apply, transform

doc/source/whatsnew/v1.5.3.rst

@@ -19,6 +19,7 @@ Fixed regressions
 - Enforced reversion of ``color`` as an alias for ``c`` and ``size`` as an alias for ``s`` in function :meth:`DataFrame.plot.scatter` (:issue:`49732`)
 - Fixed regression in :meth:`SeriesGroupBy.apply` setting a ``name`` attribute on the result if the result was a :class:`DataFrame` (:issue:`49907`)
 - Fixed performance regression in setting with the :meth:`~DataFrame.at` indexer (:issue:`49771`)
+- Fixed regression in the methods ``apply``, ``agg``, and ``transform`` when used with NumPy functions that informed users to supply ``numeric_only=True`` if the operation failed on non-numeric dtypes; such columns must be dropped prior to using these methods (:issue:`50538`)
 -
 
 .. ---------------------------------------------------------------------------

pandas/core/apply.py

@@ -39,7 +39,10 @@
     SpecificationError,
 )
 from pandas.util._decorators import cache_readonly
-from pandas.util._exceptions import find_stack_level
+from pandas.util._exceptions import (
+    find_stack_level,
+    rewrite_warning,
+)
 
 from pandas.core.dtypes.cast import is_nested_object
 from pandas.core.dtypes.common import (
@@ -174,7 +177,15 @@ def agg(self) -> DataFrame | Series | None:
         if callable(arg):
             f = com.get_cython_func(arg)
             if f and not args and not kwargs:
-                return getattr(obj, f)()
+                # GH#50538
+                old_msg = "The default value of numeric_only"
+                new_msg = (
+                    f"The operation {arg} failed on a column. If any error is "
+                    f"raised, this will raise an exception in a future version "
+                    f"of pandas. Drop these columns to avoid this warning."
+                )
+                with rewrite_warning(old_msg, FutureWarning, new_msg):
+                    return getattr(obj, f)()
 
         # caller can react
         return None
@@ -309,7 +320,14 @@ def transform_str_or_callable(self, func) -> DataFrame | Series:
         if not args and not kwargs:
             f = com.get_cython_func(func)
             if f:
-                return getattr(obj, f)()
+                old_msg = "The default value of numeric_only"
+                new_msg = (
+                    f"The operation {func} failed on a column. If any error is "
+                    f"raised, this will raise an exception in a future version "
+                    f"of pandas. Drop these columns to avoid this warning."
+                )
+                with rewrite_warning(old_msg, FutureWarning, new_msg):
+                    return getattr(obj, f)()
 
         # Two possible ways to use a UDF - apply or call directly
         try:

pandas/core/groupby/groupby.py

@@ -8,7 +8,10 @@ class providing the base-class of operations.
 """
 from __future__ import annotations
 
-from contextlib import contextmanager
+from contextlib import (
+    contextmanager,
+    nullcontext,
+)
 import datetime
 from functools import (
     partial,
@@ -64,7 +67,10 @@ class providing the base-class of operations.
     cache_readonly,
     doc,
 )
-from pandas.util._exceptions import find_stack_level
+from pandas.util._exceptions import (
+    find_stack_level,
+    rewrite_warning,
+)
 
 from pandas.core.dtypes.cast import ensure_dtype_can_hold_na
 from pandas.core.dtypes.common import (
@@ -1508,7 +1514,9 @@ def _aggregate_with_numba(self, data, func, *args, engine_kwargs=None, **kwargs)
         )
     )
     def apply(self, func, *args, **kwargs) -> NDFrameT:
-
+        # GH#50538
+        is_np_func = func in com._cython_table and func not in com._builtin_table
+        orig_func = func
         func = com.is_builtin_func(func)
 
         if isinstance(func, str):
@@ -1546,7 +1554,17 @@ def f(g):
         # ignore SettingWithCopy here in case the user mutates
         with option_context("mode.chained_assignment", None):
             try:
-                result = self._python_apply_general(f, self._selected_obj)
+                # GH#50538
+                old_msg = "The default value of numeric_only"
+                new_msg = (
+                    f"The operation {orig_func} failed on a column. If any error is "
+                    f"raised, this will raise an exception in a future version "
+                    f"of pandas. Drop these columns to avoid this warning."
+                )
+                with rewrite_warning(
+                    old_msg, FutureWarning, new_msg
+                ) if is_np_func else nullcontext():
+                    result = self._python_apply_general(f, self._selected_obj)
             except TypeError:
                 # gh-20949
                 # try again, with .apply acting as a filtering
@@ -1557,7 +1575,17 @@ def f(g):
                 # on a string grouper column
 
                 with self._group_selection_context():
-                    return self._python_apply_general(f, self._selected_obj)
+                    # GH#50538
+                    old_msg = "The default value of numeric_only"
+                    new_msg = (
+                        f"The operation {orig_func} failed on a column. If any error "
+                        f"is raised, this will raise an exception in a future version "
+                        f"of pandas. Drop these columns to avoid this warning."
+                    )
+                    with rewrite_warning(
+                        old_msg, FutureWarning, new_msg
+                    ) if is_np_func else nullcontext():
+                        return self._python_apply_general(f, self._selected_obj)
 
         return result
 

pandas/tests/apply/test_frame_apply.py

@@ -1287,6 +1287,27 @@ def test_nuiscance_columns():
     tm.assert_frame_equal(result, expected)
 
 
+@pytest.mark.parametrize("method", ["agg", "apply", "transform"])
+def test_numeric_only_warning_numpy(method):
+    # GH#50538
+    df = DataFrame({"a": [1, 1, 2], "b": list("xyz")})
+    if method == "agg":
+        msg = "The operation <function mean.*failed"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            getattr(df, method)(np.mean)
+        # Ensure users can't pass numeric_only
+        with pytest.raises(TypeError, match="got an unexpected keyword argument"):
+            getattr(df, method)(np.mean, numeric_only=True)
+    elif method == "apply":
+        with pytest.raises(TypeError, match="Could not convert"):
+            getattr(df, method)(np.mean)
+    else:
+        with pytest.raises(ValueError, match="Function did not transform"):
+            msg = "The operation <function mean.*failed"
+            with tm.assert_produces_warning(FutureWarning, match=msg):
+                getattr(df, method)(np.mean)
+
+
 @pytest.mark.parametrize("how", ["agg", "apply"])
 def test_non_callable_aggregates(how):
 

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -1454,3 +1454,15 @@ def test_agg_of_mode_list(test, constant):
     expected = expected.set_index(0)
 
     tm.assert_frame_equal(result, expected)
+
+
+def test_numeric_only_warning_numpy():
+    # GH#50538
+    df = DataFrame({"a": [1, 1, 2], "b": list("xyz"), "c": [3, 4, 5]})
+    gb = df.groupby("a")
+    msg = "The operation <function mean.*failed"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        gb.agg(np.mean)
+    # Ensure users can't pass numeric_only
+    with pytest.raises(TypeError, match="got an unexpected keyword argument"):
+        gb.agg(np.mean, numeric_only=True)

pandas/tests/groupby/test_apply.py

@@ -1357,3 +1357,16 @@ def test_empty_df(method, op):
     )
 
     tm.assert_series_equal(result, expected)
+
+
+def test_numeric_only_warning_numpy():
+    # GH#50538
+    df = DataFrame({"a": [1, 1, 2], "b": list("xyz"), "c": [3, 4, 5]})
+    gb = df.groupby("a")
+    msg = "The operation <function mean.*failed"
+    # Warning is raised from within NumPy
+    with tm.assert_produces_warning(FutureWarning, match=msg, check_stacklevel=False):
+        gb.apply(np.mean)
+    # Ensure users can't pass numeric_only
+    with pytest.raises(TypeError, match="got an unexpected keyword argument"):
+        gb.apply(np.mean, numeric_only=True)

pandas/tests/groupby/test_groupby.py

@@ -486,9 +486,14 @@ def test_frame_set_name_single(df):
         result = df.groupby("A", as_index=False).mean()
     assert result.index.name != "A"
 
+    # GH#50538
+    msg = "The operation <function mean.*failed"
     with tm.assert_produces_warning(FutureWarning, match=msg):
         result = grouped.agg(np.mean)
     assert result.index.name == "A"
+    # Ensure users can't pass numeric_only
+    with pytest.raises(TypeError, match="got an unexpected keyword argument"):
+        grouped.agg(np.mean, numeric_only=True)
 
     result = grouped.agg({"C": np.mean, "D": np.std})
     assert result.index.name == "A"
@@ -766,19 +771,24 @@ def test_as_index_series_return_frame(df):
     grouped = df.groupby("A", as_index=False)
     grouped2 = df.groupby(["A", "B"], as_index=False)
 
-    msg = "The default value of numeric_only"
+    # GH#50538
+    msg = "The operation <function sum.*failed"
     with tm.assert_produces_warning(FutureWarning, match=msg):
         result = grouped["C"].agg(np.sum)
         expected = grouped.agg(np.sum).loc[:, ["A", "C"]]
     assert isinstance(result, DataFrame)
     tm.assert_frame_equal(result, expected)
+    # Ensure users can't pass numeric_only
+    with pytest.raises(TypeError, match="got an unexpected keyword argument"):
+        grouped.agg(np.mean, numeric_only=True)
 
     result2 = grouped2["C"].agg(np.sum)
     expected2 = grouped2.agg(np.sum).loc[:, ["A", "B", "C"]]
     assert isinstance(result2, DataFrame)
     tm.assert_frame_equal(result2, expected2)
 
     result = grouped["C"].sum()
+    msg = "The default value of numeric_only"
     with tm.assert_produces_warning(FutureWarning, match=msg):
         expected = grouped.sum().loc[:, ["A", "C"]]
     assert isinstance(result, DataFrame)
@@ -1021,10 +1031,14 @@ def test_wrap_aggregated_output_multindex(mframe):
     df["baz", "two"] = "peekaboo"
 
     keys = [np.array([0, 0, 1]), np.array([0, 0, 1])]
-    msg = "The default value of numeric_only"
+    # GH#50538
+    msg = "The operation <function mean.*failed"
     with tm.assert_produces_warning(FutureWarning, match=msg):
         agged = df.groupby(keys).agg(np.mean)
     assert isinstance(agged.columns, MultiIndex)
+    # Ensure users can't pass numeric_only
+    with pytest.raises(TypeError, match="got an unexpected keyword argument"):
+        df.groupby(keys).agg(np.mean, numeric_only=True)
 
     def aggfun(ser):
         if ser.name == ("foo", "one"):

pandas/tests/groupby/transform/test_transform.py

@@ -1563,3 +1563,18 @@ def test_as_index_no_change(keys, df, groupby_func):
     result = gb_as_index_true.transform(groupby_func, *args)
     expected = gb_as_index_false.transform(groupby_func, *args)
     tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize("func", [np.mean, np.cumprod])
+def test_numeric_only_warning_numpy(func):
+    # GH#50538
+    df = DataFrame({"a": [1, 1, 2], "b": list("xyz"), "c": [3, 4, 5]})
+    gb = df.groupby("a")
+    msg = "The default value of numeric_only"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        gb.transform(func)
+    # Ensure users can pass numeric_only
+    result = gb.transform(func, numeric_only=True)
+    values = [3.5, 3.5, 5.0] if func == np.mean else [3, 12, 5]
+    expected = DataFrame({"c": values})
+    tm.assert_frame_equal(result, expected)

pandas/tests/resample/test_resample_api.py

@@ -938,3 +938,24 @@ def test_series_downsample_method(method, numeric_only, expected_data):
         result = func(numeric_only=numeric_only)
         expected = Series(expected_data, index=expected_index)
         tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("method", ["agg", "apply", "transform"])
+def test_numeric_only_warning_numpy(method):
+    # GH#50538
+    resampled = _test_frame.assign(D="x").resample("H")
+    if method == "transform":
+        msg = "The default value of numeric_only"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            getattr(resampled, method)(np.mean)
+        # Ensure users can pass numeric_only
+        result = getattr(resampled, method)(np.mean, numeric_only=True)
+        expected = resampled.transform("mean", numeric_only=True)
+        tm.assert_frame_equal(result, expected)
+    else:
+        msg = "The operation <function mean.*failed"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            getattr(resampled, method)(np.mean)
+        # Ensure users can't pass numeric_only
+        with pytest.raises(TypeError, match="got an unexpected keyword argument"):
+            getattr(resampled, method)(np.mean, numeric_only=True)

pandas/tests/reshape/test_pivot.py

@@ -146,7 +146,8 @@ def test_pivot_table_nocols(self):
         df = DataFrame(
             {"rows": ["a", "b", "c"], "cols": ["x", "y", "z"], "values": [1, 2, 3]}
         )
-        msg = "pivot_table dropped a column because it failed to aggregate"
+        # GH#50538
+        msg = "The operation <function sum.*failed"
         with tm.assert_produces_warning(FutureWarning, match=msg):
             rs = df.pivot_table(columns="cols", aggfunc=np.sum)
             xp = df.pivot_table(index="cols", aggfunc=np.sum).T
@@ -907,7 +908,8 @@ def test_no_col(self):
 
         # to help with a buglet
         self.data.columns = [k * 2 for k in self.data.columns]
-        msg = "pivot_table dropped a column because it failed to aggregate"
+        # GH#50538
+        msg = "The operation <function mean.*failed"
         with tm.assert_produces_warning(FutureWarning, match=msg):
             table = self.data.pivot_table(
                 index=["AA", "BB"], margins=True, aggfunc=np.mean
@@ -916,6 +918,7 @@ def test_no_col(self):
             totals = table.loc[("All", ""), value_col]
             assert totals == self.data[value_col].mean()
 
+        msg = "pivot_table dropped a column because it failed to aggregate"
         with tm.assert_produces_warning(FutureWarning, match=msg):
             table = self.data.pivot_table(
                 index=["AA", "BB"], margins=True, aggfunc="mean"
@@ -975,7 +978,11 @@ def test_margin_with_only_columns_defined(
             }
         )
 
-        msg = "pivot_table dropped a column because it failed to aggregate"
+        if aggfunc == "sum":
+            msg = "pivot_table dropped a column because it failed to aggregate"
+        else:
+            # GH#50538
+            msg = "The operation <function mean.*failed"
         with tm.assert_produces_warning(FutureWarning, match=msg):
             result = df.pivot_table(columns=columns, margins=True, aggfunc=aggfunc)
         expected = DataFrame(values, index=Index(["D", "E"]), columns=expected_columns)