From 0c4aea5dfc60b990acaf69a0e744ace36a78310a Mon Sep 17 00:00:00 2001
From: Richard Shadrach <rhshadrach@gmail.com>
Date: Thu, 10 Nov 2022 12:33:44 -0500
Subject: [PATCH 1/5] DEPR: Enforce default of numeric_only=False

---
 doc/source/whatsnew/v2.0.0.rst              |  2 +-
 pandas/core/frame.py                        | 69 ++++++---------------
 pandas/core/generic.py                      | 36 +----------
 pandas/tests/apply/test_frame_transform.py  | 46 +++++---------
 pandas/tests/apply/test_series_apply.py     |  4 +-
 pandas/tests/frame/methods/test_cov_corr.py | 33 +++++-----
 pandas/tests/frame/methods/test_quantile.py | 31 ---------
 pandas/tests/frame/methods/test_rank.py     | 15 ++---
 pandas/tests/frame/test_reductions.py       | 39 +-----------
 9 files changed, 61 insertions(+), 214 deletions(-)

diff --git a/doc/source/whatsnew/v2.0.0.rst b/doc/source/whatsnew/v2.0.0.rst
index 73a75667b46da..3295e93ab21a0 100644
--- a/doc/source/whatsnew/v2.0.0.rst
+++ b/doc/source/whatsnew/v2.0.0.rst
@@ -502,7 +502,7 @@ Removal of prior version deprecations/changes
 - Removed ``na_sentinel`` argument from :func:`factorize`, :meth:`.Index.factorize`, and :meth:`.ExtensionArray.factorize` (:issue:`47157`)
 - Changed behavior of :meth:`DataFrameGroupBy.apply` and :meth:`SeriesGroupBy.apply` so that ``group_keys`` is respected even if a transformer is detected (:issue:`34998`)
 - 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`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_200.performance:
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 1627a7add25ed..ff795b0a9589c 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -9939,7 +9939,7 @@ def corr(
         self,
         method: CorrelationMethod = "pearson",
         min_periods: int = 1,
-        numeric_only: bool | lib.NoDefault = lib.no_default,
+        numeric_only: bool = False,
     ) -> DataFrame:
         """
         Compute pairwise correlation of columns, excluding NA/null values.
@@ -9960,15 +9960,11 @@ def corr(
             Minimum number of observations required per pair of columns
             to have a valid result. Currently only available for Pearson
             and Spearman correlation.
-        numeric_only : bool, default True
+        numeric_only : bool, default False
             Include only `float`, `int` or `boolean` data.
 
             .. versionadded:: 1.5.0
 
-            .. deprecated:: 1.5.0
-                The default value of ``numeric_only`` will be ``False`` in a future
-                version of pandas.
-
         Returns
         -------
         DataFrame
@@ -10007,11 +10003,7 @@ def corr(
         dogs   1.0   NaN
         cats   NaN   1.0
         """  # noqa:E501
-        numeric_only_bool = com.resolve_numeric_only(numeric_only)
-        data = self._get_numeric_data() if numeric_only_bool else self
-        if numeric_only is lib.no_default and len(data.columns) < len(self.columns):
-            com.deprecate_numeric_only_default(type(self), "corr")
-
+        data = self._get_numeric_data() if numeric_only else self
         cols = data.columns
         idx = cols.copy()
         mat = data.to_numpy(dtype=float, na_value=np.nan, copy=False)
@@ -10058,7 +10050,7 @@ def cov(
         self,
         min_periods: int | None = None,
         ddof: int | None = 1,
-        numeric_only: bool | lib.NoDefault = lib.no_default,
+        numeric_only: bool = False,
     ) -> DataFrame:
         """
         Compute pairwise covariance of columns, excluding NA/null values.
@@ -10090,15 +10082,11 @@ def cov(
 
             .. versionadded:: 1.1.0
 
-        numeric_only : bool, default True
+        numeric_only : bool, default False
             Include only `float`, `int` or `boolean` data.
 
             .. versionadded:: 1.5.0
 
-            .. deprecated:: 1.5.0
-                The default value of ``numeric_only`` will be ``False`` in a future
-                version of pandas.
-
         Returns
         -------
         DataFrame
@@ -10168,11 +10156,7 @@ def cov(
         b       NaN  1.248003  0.191417
         c -0.150812  0.191417  0.895202
         """
-        numeric_only_bool = com.resolve_numeric_only(numeric_only)
-        data = self._get_numeric_data() if numeric_only_bool else self
-        if numeric_only is lib.no_default and len(data.columns) < len(self.columns):
-            com.deprecate_numeric_only_default(type(self), "cov")
-
+        data = self._get_numeric_data() if numeric_only else self
         cols = data.columns
         idx = cols.copy()
         mat = data.to_numpy(dtype=float, na_value=np.nan, copy=False)
@@ -10196,7 +10180,7 @@ def corrwith(
         axis: Axis = 0,
         drop: bool = False,
         method: CorrelationMethod = "pearson",
-        numeric_only: bool | lib.NoDefault = lib.no_default,
+        numeric_only: bool = False,
     ) -> Series:
         """
         Compute pairwise correlation.
@@ -10224,15 +10208,11 @@ def corrwith(
             * callable: callable with input two 1d ndarrays
                 and returning a float.
 
-        numeric_only : bool, default True
+        numeric_only : bool, default False
             Include only `float`, `int` or `boolean` data.
 
             .. versionadded:: 1.5.0
 
-            .. deprecated:: 1.5.0
-                The default value of ``numeric_only`` will be ``False`` in a future
-                version of pandas.
-
         Returns
         -------
         Series
@@ -10264,15 +10244,12 @@ def corrwith(
         dtype: float64
         """  # noqa:E501
         axis = self._get_axis_number(axis)
-        numeric_only_bool = com.resolve_numeric_only(numeric_only)
-        this = self._get_numeric_data() if numeric_only_bool else self
-        if numeric_only is lib.no_default and len(this.columns) < len(self.columns):
-            com.deprecate_numeric_only_default(type(self), "corrwith")
+        this = self._get_numeric_data() if numeric_only else self
 
         if isinstance(other, Series):
             return this.apply(lambda x: other.corr(x, method=method), axis=axis)
 
-        if numeric_only_bool:
+        if numeric_only:
             other = other._get_numeric_data()
         left, right = this.align(other, join="inner", copy=False)
 
@@ -10286,14 +10263,14 @@ def corrwith(
             right = right + left * 0
 
             # demeaned data
-            ldem = left - left.mean(numeric_only=numeric_only_bool)
-            rdem = right - right.mean(numeric_only=numeric_only_bool)
+            ldem = left - left.mean(numeric_only=numeric_only)
+            rdem = right - right.mean(numeric_only=numeric_only)
 
             num = (ldem * rdem).sum()
             dom = (
                 (left.count() - 1)
-                * left.std(numeric_only=numeric_only_bool)
-                * right.std(numeric_only=numeric_only_bool)
+                * left.std(numeric_only=numeric_only)
+                * right.std(numeric_only=numeric_only)
             )
 
             correl = num / dom
@@ -10801,7 +10778,7 @@ def quantile(
         self,
         q: float = ...,
         axis: Axis = ...,
-        numeric_only: bool | lib.NoDefault = ...,
+        numeric_only: bool = ...,
         interpolation: QuantileInterpolation = ...,
     ) -> Series:
         ...
@@ -10811,7 +10788,7 @@ def quantile(
         self,
         q: AnyArrayLike | Sequence[float],
         axis: Axis = ...,
-        numeric_only: bool | lib.NoDefault = ...,
+        numeric_only: bool = ...,
         interpolation: QuantileInterpolation = ...,
     ) -> Series | DataFrame:
         ...
@@ -10821,7 +10798,7 @@ def quantile(
         self,
         q: float | AnyArrayLike | Sequence[float] = ...,
         axis: Axis = ...,
-        numeric_only: bool | lib.NoDefault = ...,
+        numeric_only: bool = ...,
         interpolation: QuantileInterpolation = ...,
     ) -> Series | DataFrame:
         ...
@@ -10830,7 +10807,7 @@ def quantile(
         self,
         q: float | AnyArrayLike | Sequence[float] = 0.5,
         axis: Axis = 0,
-        numeric_only: bool | lib.NoDefault = no_default,
+        numeric_only: bool = False,
         interpolation: QuantileInterpolation = "linear",
         method: Literal["single", "table"] = "single",
     ) -> Series | DataFrame:
@@ -10843,13 +10820,8 @@ def quantile(
             Value between 0 <= q <= 1, the quantile(s) to compute.
         axis : {0 or 'index', 1 or 'columns'}, default 0
             Equals 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
-        numeric_only : bool, default True
-            If False, the quantile of datetime and timedelta data will be
-            computed as well.
-
-            .. deprecated:: 1.5.0
-                The default value of ``numeric_only`` will be ``False`` in a future
-                version of pandas.
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
 
         interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
             This optional parameter specifies the interpolation method to use,
@@ -10924,7 +10896,6 @@ def quantile(
         any_not_numeric = any(not is_numeric_dtype(x) for x in self.dtypes)
         if numeric_only is no_default and any_not_numeric:
             com.deprecate_numeric_only_default(type(self), "quantile")
-        numeric_only = com.resolve_numeric_only(numeric_only)
 
         if not is_list_like(q):
             # BlockManager.quantile expects listlike, so we wrap and unwrap here
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index d26a11eae9f7f..c578ff39f080b 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -8853,7 +8853,7 @@ def rank(
         self: NDFrameT,
         axis: Axis = 0,
         method: str = "average",
-        numeric_only: bool_t | None | lib.NoDefault = lib.no_default,
+        numeric_only: bool_t = False,
         na_option: str = "keep",
         ascending: bool_t = True,
         pct: bool_t = False,
@@ -8878,7 +8878,7 @@ def rank(
             * first: ranks assigned in order they appear in the array
             * dense: like 'min', but rank always increases by 1 between groups.
 
-        numeric_only : bool, optional
+        numeric_only : bool, default False
             For DataFrame objects, rank only numeric columns if set to True.
         na_option : {'keep', 'top', 'bottom'}, default 'keep'
             How to rank NaN values:
@@ -8953,20 +8953,6 @@ def rank(
         3   spider          8.0           4.0       4.0        4.0     1.000
         4    snake          NaN           NaN       NaN        5.0       NaN
         """
-        warned = False
-        if numeric_only is None:
-            # GH#45036
-            warnings.warn(
-                f"'numeric_only=None' in {type(self).__name__}.rank is deprecated "
-                "and will raise in a future version. Pass either 'True' or "
-                "'False'. 'False' will be the default.",
-                FutureWarning,
-                stacklevel=find_stack_level(),
-            )
-            warned = True
-        elif numeric_only is lib.no_default:
-            numeric_only = None
-
         axis_int = self._get_axis_number(axis)
 
         if na_option not in {"keep", "top", "bottom"}:
@@ -9002,24 +8988,6 @@ def ranker(data):
             ranks_obj = self._constructor(ranks, **data._construct_axes_dict())
             return ranks_obj.__finalize__(self, method="rank")
 
-        # if numeric_only is None, and we can't get anything, we try with
-        # numeric_only=True
-        if numeric_only is None:
-            try:
-                return ranker(self)
-            except TypeError:
-                numeric_only = True
-                if not warned:
-                    # Only warn here if we didn't already issue a warning above
-                    # GH#45036
-                    warnings.warn(
-                        f"Dropping of nuisance columns in {type(self).__name__}.rank "
-                        "is deprecated; in a future version this will raise TypeError. "
-                        "Select only valid columns before calling rank.",
-                        FutureWarning,
-                        stacklevel=find_stack_level(),
-                    )
-
         if numeric_only:
             if self.ndim == 1 and not is_numeric_dtype(self.dtype):
                 # GH#47500
diff --git a/pandas/tests/apply/test_frame_transform.py b/pandas/tests/apply/test_frame_transform.py
index 73a52534dd0d2..8e385de0b48e0 100644
--- a/pandas/tests/apply/test_frame_transform.py
+++ b/pandas/tests/apply/test_frame_transform.py
@@ -130,10 +130,6 @@ def func(x):
 frame_kernels_raise = [x for x in frame_transform_kernels if x not in wont_fail]
 
 
-@pytest.mark.filterwarnings(
-    "ignore:Calling Series.rank with numeric_only:FutureWarning"
-)
-@pytest.mark.filterwarnings("ignore:Dropping of nuisance:FutureWarning")
 @pytest.mark.parametrize("op", [*frame_kernels_raise, lambda x: x + 1])
 def test_transform_bad_dtype(op, frame_or_series, request):
     # GH 35964
@@ -144,17 +140,13 @@ def test_transform_bad_dtype(op, frame_or_series, request):
 
     obj = DataFrame({"A": 3 * [object]})  # DataFrame that will fail on most transforms
     obj = tm.get_obj(obj, frame_or_series)
-    if op == "rank":
-        error = ValueError
-        msg = "Transform function failed"
-    else:
-        error = TypeError
-        msg = "|".join(
-            [
-                "not supported between instances of 'type' and 'type'",
-                "unsupported operand type",
-            ]
-        )
+    error = TypeError
+    msg = "|".join(
+        [
+            "not supported between instances of 'type' and 'type'",
+            "unsupported operand type",
+        ]
+    )
 
     with pytest.raises(error, match=msg):
         obj.transform(op)
@@ -166,12 +158,6 @@ def test_transform_bad_dtype(op, frame_or_series, request):
         obj.transform({"A": [op]})
 
 
-@pytest.mark.filterwarnings(
-    "ignore:Dropping of nuisance columns in Series.rank:FutureWarning"
-)
-@pytest.mark.filterwarnings(
-    "ignore:Calling Series.rank with numeric_only:FutureWarning"
-)
 @pytest.mark.parametrize("op", frame_kernels_raise)
 def test_transform_failure_typeerror(request, op):
     # GH 35964
@@ -183,17 +169,13 @@ def test_transform_failure_typeerror(request, op):
 
     # Using object makes most transform kernels fail
     df = DataFrame({"A": 3 * [object], "B": [1, 2, 3]})
-    if op == "rank":
-        error = ValueError
-        msg = "Transform function failed"
-    else:
-        error = TypeError
-        msg = "|".join(
-            [
-                "not supported between instances of 'type' and 'type'",
-                "unsupported operand type",
-            ]
-        )
+    error = TypeError
+    msg = "|".join(
+        [
+            "not supported between instances of 'type' and 'type'",
+            "unsupported operand type",
+        ]
+    )
 
     with pytest.raises(error, match=msg):
         df.transform([op])
diff --git a/pandas/tests/apply/test_series_apply.py b/pandas/tests/apply/test_series_apply.py
index e0d3510ac3865..5986f1f6cf51d 100644
--- a/pandas/tests/apply/test_series_apply.py
+++ b/pandas/tests/apply/test_series_apply.py
@@ -275,8 +275,6 @@ def test_transform(string_series):
         tm.assert_series_equal(result.reindex_like(expected), expected)
 
 
-@pytest.mark.filterwarnings("ignore:Calling Series.rank:FutureWarning")
-@pytest.mark.filterwarnings("ignore:Dropping of nuisance:FutureWarning")
 @pytest.mark.parametrize("op", series_transform_kernels)
 def test_transform_partial_failure(op, request):
     # GH 35964
@@ -288,7 +286,7 @@ def test_transform_partial_failure(op, request):
     # Using object makes most transform kernels fail
     ser = Series(3 * [object])
 
-    if op in ("fillna", "ngroup", "rank"):
+    if op in ("fillna", "ngroup"):
         error = ValueError
         msg = "Transform function failed"
     else:
diff --git a/pandas/tests/frame/methods/test_cov_corr.py b/pandas/tests/frame/methods/test_cov_corr.py
index 445b90327ed2c..d7333ce03c215 100644
--- a/pandas/tests/frame/methods/test_cov_corr.py
+++ b/pandas/tests/frame/methods/test_cov_corr.py
@@ -40,11 +40,10 @@ def test_cov(self, float_frame, float_string_frame):
         expected = frame["A"].cov(frame["C"])
         tm.assert_almost_equal(result["A"]["C"], expected)
 
-        # exclude non-numeric types
-        with tm.assert_produces_warning(
-            FutureWarning, match="The default value of numeric_only"
-        ):
-            result = float_string_frame.cov()
+        # fails on non-numeric types
+        with pytest.raises(ValueError, match="could not convert string to float"):
+            float_string_frame.cov()
+        result = float_string_frame.cov(numeric_only=True)
         expected = float_string_frame.loc[:, ["A", "B", "C", "D"]].cov()
         tm.assert_frame_equal(result, expected)
 
@@ -118,11 +117,9 @@ def test_corr_scipy_method(self, float_frame, method):
     # ---------------------------------------------------------------------
 
     def test_corr_non_numeric(self, float_string_frame):
-        # exclude non-numeric types
-        with tm.assert_produces_warning(
-            FutureWarning, match="The default value of numeric_only"
-        ):
-            result = float_string_frame.corr()
+        with pytest.raises(ValueError, match="could not convert string to float"):
+            float_string_frame.corr()
+        result = float_string_frame.corr(numeric_only=True)
         expected = float_string_frame.loc[:, ["A", "B", "C", "D"]].corr()
         tm.assert_frame_equal(result, expected)
 
@@ -218,7 +215,7 @@ def test_corr_item_cache(self):
         ser = df["A"]  # populate item_cache
         assert len(df._mgr.arrays) == 2  # i.e. 2 blocks
 
-        _ = df.corr()
+        _ = df.corr(numeric_only=True)
 
         # Check that the corr didn't break link between ser and df
         ser.values[0] = 99
@@ -313,17 +310,15 @@ def test_corrwith_with_objects(self):
         df1["obj"] = "foo"
         df2["obj"] = "bar"
 
-        with tm.assert_produces_warning(
-            FutureWarning, match="The default value of numeric_only"
-        ):
-            result = df1.corrwith(df2)
+        with pytest.raises(TypeError, match="Could not convert"):
+            df1.corrwith(df2)
+        result = df1.corrwith(df2, numeric_only=True)
         expected = df1.loc[:, cols].corrwith(df2.loc[:, cols])
         tm.assert_series_equal(result, expected)
 
-        with tm.assert_produces_warning(
-            FutureWarning, match="The default value of numeric_only"
-        ):
-            result = df1.corrwith(df2, axis=1)
+        with pytest.raises(TypeError, match="unsupported operand type"):
+            df1.corrwith(df2, axis=1)
+        result = df1.corrwith(df2, axis=1, numeric_only=True)
         expected = df1.loc[:, cols].corrwith(df2.loc[:, cols], axis=1)
         tm.assert_series_equal(result, expected)
 
diff --git a/pandas/tests/frame/methods/test_quantile.py b/pandas/tests/frame/methods/test_quantile.py
index b4661a92c8275..8096af757d3cf 100644
--- a/pandas/tests/frame/methods/test_quantile.py
+++ b/pandas/tests/frame/methods/test_quantile.py
@@ -25,37 +25,6 @@ def interp_method(request):
 
 
 class TestDataFrameQuantile:
-    @pytest.mark.parametrize(
-        "non_num_col",
-        [
-            pd.date_range("2014-01-01", periods=3, freq="m"),
-            ["a", "b", "c"],
-            [DataFrame, Series, Timestamp],
-        ],
-    )
-    def test_numeric_only_default_false_warning(
-        self, non_num_col, interp_method, request, using_array_manager
-    ):
-        # GH #7308
-        interpolation, method = interp_method
-        df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]})
-        df["C"] = non_num_col
-
-        expected = Series(
-            [2.0, 3.0],
-            index=["A", "B"],
-            name=0.5,
-        )
-        if interpolation == "nearest":
-            expected = expected.astype(np.int64)
-        if method == "table" and using_array_manager:
-            request.node.add_marker(
-                pytest.mark.xfail(reason="Axis name incorrectly set.")
-            )
-        with tm.assert_produces_warning(FutureWarning, match="numeric_only"):
-            result = df.quantile(0.5, interpolation=interpolation, method=method)
-        tm.assert_series_equal(result, expected)
-
     @pytest.mark.parametrize(
         "df,expected",
         [
diff --git a/pandas/tests/frame/methods/test_rank.py b/pandas/tests/frame/methods/test_rank.py
index 1f5cb95885004..5f648c76d0aa4 100644
--- a/pandas/tests/frame/methods/test_rank.py
+++ b/pandas/tests/frame/methods/test_rank.py
@@ -136,12 +136,9 @@ def test_rank_mixed_frame(self, float_string_frame):
         float_string_frame["datetime"] = datetime.now()
         float_string_frame["timedelta"] = timedelta(days=1, seconds=1)
 
-        with tm.assert_produces_warning(FutureWarning, match="numeric_only=None"):
-            float_string_frame.rank(numeric_only=None)
-        with tm.assert_produces_warning(FutureWarning, match="Dropping of nuisance"):
-            result = float_string_frame.rank(1)
-        expected = float_string_frame.rank(1, numeric_only=True)
-        tm.assert_frame_equal(result, expected)
+        float_string_frame.rank(numeric_only=False)
+        with pytest.raises(TypeError, match="not supported between instances of"):
+            float_string_frame.rank(axis=1)
 
     @td.skip_if_no_scipy
     def test_rank_na_option(self, float_frame):
@@ -491,7 +488,7 @@ def test_rank_object_first(self, frame_or_series, na_option, ascending, expected
     )
     def test_rank_mixed_axis_zero(self, data, expected):
         df = DataFrame(data)
-        msg = "Dropping of nuisance columns"
-        with tm.assert_produces_warning(FutureWarning, match=msg):
-            result = df.rank()
+        with pytest.raises(TypeError, match="'<' not supported between instances of"):
+            df.rank()
+        result = df.rank(numeric_only=True)
         tm.assert_frame_equal(result, expected)
diff --git a/pandas/tests/frame/test_reductions.py b/pandas/tests/frame/test_reductions.py
index 0e5c6057b9a61..d90cdd664258f 100644
--- a/pandas/tests/frame/test_reductions.py
+++ b/pandas/tests/frame/test_reductions.py
@@ -1,13 +1,11 @@
 from datetime import timedelta
 from decimal import Decimal
-import inspect
 import re
 
 from dateutil.tz import tzlocal
 import numpy as np
 import pytest
 
-from pandas._libs import lib
 from pandas.compat import is_platform_windows
 import pandas.util._test_decorators as td
 
@@ -1697,40 +1695,7 @@ def test_reduction_axis_none_deprecation(method):
     [
         "corr",
         "corrwith",
-        "count",
         "cov",
-        "mode",
-        "quantile",
-    ],
-)
-def test_numeric_only_deprecation(kernel):
-    # GH#46852
-    df = DataFrame({"a": [1, 2, 3], "b": object})
-    args = (df,) if kernel == "corrwith" else ()
-    signature = inspect.signature(getattr(DataFrame, kernel))
-    default = signature.parameters["numeric_only"].default
-    assert default is not True
-
-    if default is None or default is lib.no_default:
-        expected = getattr(df[["a"]], kernel)(*args)
-        warn = FutureWarning
-    else:
-        # default must be False and works on any nuisance columns
-        expected = getattr(df, kernel)(*args)
-        if kernel == "mode":
-            assert "b" in expected.columns
-        else:
-            assert "b" in expected.index
-        warn = None
-    msg = f"The default value of numeric_only in DataFrame.{kernel}"
-    with tm.assert_produces_warning(warn, match=msg):
-        result = getattr(df, kernel)(*args)
-    tm.assert_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "kernel",
-    [
         "idxmax",
         "idxmin",
         "kurt",
@@ -1739,6 +1704,7 @@ def test_numeric_only_deprecation(kernel):
         "median",
         "min",
         "prod",
+        "quantile",
         "sem",
         "skew",
         "std",
@@ -1749,6 +1715,7 @@ def test_numeric_only_deprecation(kernel):
 def test_fails_on_non_numeric(kernel):
     # GH#46852
     df = DataFrame({"a": [1, 2, 3], "b": object})
+    args = (df,) if kernel == "corrwith" else ()
     msg = "|".join(
         [
             "not allowed for this dtype",
@@ -1759,4 +1726,4 @@ def test_fails_on_non_numeric(kernel):
         ]
     )
     with pytest.raises(TypeError, match=msg):
-        getattr(df, kernel)()
+        getattr(df, kernel)(*args)

From cc1e87619ddd29c21bc3977c26bf12cfdc4a968c Mon Sep 17 00:00:00 2001
From: Richard Shadrach <rhshadrach@gmail.com>
Date: Thu, 10 Nov 2022 12:36:19 -0500
Subject: [PATCH 2/5] Remove unused functions

---
 pandas/core/common.py | 64 -------------------------------------------
 pandas/core/frame.py  | 10 -------
 2 files changed, 74 deletions(-)

diff --git a/pandas/core/common.py b/pandas/core/common.py
index c73c31c2a103b..8764ee0ea6ed7 100644
--- a/pandas/core/common.py
+++ b/pandas/core/common.py
@@ -25,7 +25,6 @@
     cast,
     overload,
 )
-import warnings
 
 import numpy as np
 
@@ -37,7 +36,6 @@
     RandomState,
     T,
 )
-from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
 from pandas.core.dtypes.common import (
@@ -631,65 +629,3 @@ def fill_missing_names(names: Sequence[Hashable | None]) -> list[Hashable]:
         list of column names with the None values replaced.
     """
     return [f"level_{i}" if name is None else name for i, name in enumerate(names)]
-
-
-def resolve_numeric_only(numeric_only: bool | None | lib.NoDefault) -> bool:
-    """Determine the Boolean value of numeric_only.
-
-    See GH#46560 for details on the deprecation.
-
-    Parameters
-    ----------
-    numeric_only : bool, None, or lib.no_default
-        Value passed to the method.
-
-    Returns
-    -------
-    Resolved value of numeric_only.
-    """
-    if numeric_only is lib.no_default:
-        # Methods that behave like numeric_only=True and only got the numeric_only
-        # arg in 1.5.0 default to lib.no_default
-        result = True
-    elif numeric_only is None:
-        # Methods that had the numeric_only arg prior to 1.5.0 and try all columns
-        # first default to None
-        result = False
-    else:
-        result = numeric_only
-    return result
-
-
-def deprecate_numeric_only_default(
-    cls: type, name: str, deprecate_none: bool = False
-) -> None:
-    """Emit FutureWarning message for deprecation of numeric_only.
-
-    See GH#46560 for details on the deprecation.
-
-    Parameters
-    ----------
-    cls : type
-        pandas type that is generating the warning.
-    name : str
-        Name of the method that is generating the warning.
-    deprecate_none : bool, default False
-        Whether to also warn about the deprecation of specifying ``numeric_only=None``.
-    """
-    if name in ["all", "any"]:
-        arg_name = "bool_only"
-    else:
-        arg_name = "numeric_only"
-
-    msg = (
-        f"The default value of {arg_name} in {cls.__name__}.{name} is "
-        "deprecated. In a future version, it will default to False. "
-    )
-    if deprecate_none:
-        msg += f"In addition, specifying '{arg_name}=None' is deprecated. "
-    msg += (
-        f"Select only valid columns or specify the value of {arg_name} to silence "
-        "this warning."
-    )
-
-    warnings.warn(msg, FutureWarning, stacklevel=find_stack_level())
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index ff795b0a9589c..21be0e1d274ed 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -133,7 +133,6 @@
     is_integer_dtype,
     is_iterator,
     is_list_like,
-    is_numeric_dtype,
     is_object_dtype,
     is_scalar,
     is_sequence,
@@ -10523,12 +10522,6 @@ def _get_data() -> DataFrame:
                 #  float64, see test_apply_funcs_over_empty
                 out = out.astype(np.float64)
 
-            if numeric_only is None and out.shape[0] != df.shape[1]:
-                # columns have been dropped GH#41480
-                com.deprecate_numeric_only_default(
-                    type(self), name, deprecate_none=True
-                )
-
             return out
 
         assert not numeric_only and axis == 1
@@ -10893,9 +10886,6 @@ def quantile(
         """
         validate_percentile(q)
         axis = self._get_axis_number(axis)
-        any_not_numeric = any(not is_numeric_dtype(x) for x in self.dtypes)
-        if numeric_only is no_default and any_not_numeric:
-            com.deprecate_numeric_only_default(type(self), "quantile")
 
         if not is_list_like(q):
             # BlockManager.quantile expects listlike, so we wrap and unwrap here

From c5d6f4e19ef886bc5f41d8a3422f8dfe1970a7ce Mon Sep 17 00:00:00 2001
From: Richard Shadrach <rhshadrach@gmail.com>
Date: Thu, 10 Nov 2022 15:22:01 -0500
Subject: [PATCH 3/5] Add versionchanged

---
 pandas/core/frame.py   | 12 ++++++++++++
 pandas/core/generic.py |  4 ++++
 2 files changed, 16 insertions(+)

diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 21be0e1d274ed..ff25cb2f2b6ad 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -9964,6 +9964,9 @@ def corr(
 
             .. versionadded:: 1.5.0
 
+            .. versionchanged:: 2.0.0
+                The default value of ``numeric_only`` is now ``False``.
+
         Returns
         -------
         DataFrame
@@ -10086,6 +10089,9 @@ def cov(
 
             .. versionadded:: 1.5.0
 
+            .. versionchanged:: 2.0.0
+                The default value of ``numeric_only`` is now ``False``.
+
         Returns
         -------
         DataFrame
@@ -10212,6 +10218,9 @@ def corrwith(
 
             .. versionadded:: 1.5.0
 
+            .. versionchanged:: 2.0.0
+                The default value of ``numeric_only`` is now ``False``.
+
         Returns
         -------
         Series
@@ -10816,6 +10825,9 @@ def quantile(
         numeric_only : bool, default False
             Include only `float`, `int` or `boolean` data.
 
+            .. versionchanged:: 2.0.0
+                The default value of ``numeric_only`` is now ``False``.
+
         interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
             This optional parameter specifies the interpolation method to use,
             when the desired quantile lies between two data points `i` and `j`:
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index c578ff39f080b..2cc3cbf04a927 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -8880,6 +8880,10 @@ def rank(
 
         numeric_only : bool, default False
             For DataFrame objects, rank only numeric columns if set to True.
+
+            .. versionchanged:: 2.0.0
+                The default value of ``numeric_only`` is now ``False``.
+
         na_option : {'keep', 'top', 'bottom'}, default 'keep'
             How to rank NaN values:
 

From e13e620382e42bc2bf8c15c26029b3209f9a2711 Mon Sep 17 00:00:00 2001
From: Richard Shadrach <rhshadrach@gmail.com>
Date: Thu, 10 Nov 2022 15:36:07 -0500
Subject: [PATCH 4/5] Add Series.rank to whatsnew

---
 doc/source/whatsnew/v2.0.0.rst | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/doc/source/whatsnew/v2.0.0.rst b/doc/source/whatsnew/v2.0.0.rst
index 1d3facce50eb6..218a7c1cf077c 100644
--- a/doc/source/whatsnew/v2.0.0.rst
+++ b/doc/source/whatsnew/v2.0.0.rst
@@ -505,7 +505,8 @@ Removal of prior version deprecations/changes
 - Changed behavior of :meth:`DataFrameGroupBy.apply` and :meth:`SeriesGroupBy.apply` so that ``group_keys`` is respected even if a transformer is detected (:issue:`34998`)
 - 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`)
+-
 .. ---------------------------------------------------------------------------
 .. _whatsnew_200.performance:
 

From fa151f0334d9794dc7e135c4cb385f5ea48dc13a Mon Sep 17 00:00:00 2001
From: Richard Shadrach <rhshadrach@gmail.com>
Date: Fri, 11 Nov 2022 08:01:23 -0500
Subject: [PATCH 5/5] newline in docs

---
 doc/source/whatsnew/v2.0.0.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/doc/source/whatsnew/v2.0.0.rst b/doc/source/whatsnew/v2.0.0.rst
index 218a7c1cf077c..85d77ccad0b32 100644
--- a/doc/source/whatsnew/v2.0.0.rst
+++ b/doc/source/whatsnew/v2.0.0.rst
@@ -507,6 +507,7 @@ 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`)
 -
+
 .. ---------------------------------------------------------------------------
 .. _whatsnew_200.performance: