BUG: validate_bool_kwarg for numeric_only

Author: jbrockmendel

pandas/core/frame.py

@@ -11430,6 +11430,7 @@ def corr(
         dogs   1.0   NaN
         cats   NaN   1.0
         """  # noqa: E501
+        validate_bool_kwarg(numeric_only, "numeric_only")
         data = self._get_numeric_data() if numeric_only else self
         cols = data.columns
         idx = cols.copy()
@@ -11586,6 +11587,7 @@ def cov(
         b       NaN  1.248003  0.191417
         c -0.150812  0.191417  0.895202
         """
+        validate_bool_kwarg(numeric_only, "numeric_only")
         data = self._get_numeric_data() if numeric_only else self
         if any(blk.dtype.kind in "mM" for blk in self._mgr.blocks):
             msg = (
@@ -11690,6 +11692,7 @@ def corrwith(
         e    NaN
         dtype: float64
         """
+        validate_bool_kwarg(numeric_only, "numeric_only")
         axis = self._get_axis_number(axis)
         this = self._get_numeric_data() if numeric_only else self
 
@@ -11825,6 +11828,7 @@ def count(self, axis: Axis = 0, numeric_only: bool = False) -> Series:
         4    3
         dtype: int64
         """
+        validate_bool_kwarg(numeric_only, "numeric_only")
         axis = self._get_axis_number(axis)
 
         if numeric_only:
@@ -13073,6 +13077,7 @@ def cummin(
         *args,
         **kwargs,
     ) -> Self:
+        validate_bool_kwarg(numeric_only, "numeric_only")
         data = self._get_numeric_data() if numeric_only else self
         return NDFrame.cummin(data, axis, skipna, *args, **kwargs)
 
@@ -13085,6 +13090,7 @@ def cummax(
         *args,
         **kwargs,
     ) -> Self:
+        validate_bool_kwarg(numeric_only, "numeric_only")
         data = self._get_numeric_data() if numeric_only else self
         return NDFrame.cummax(data, axis, skipna, *args, **kwargs)
 
@@ -13097,6 +13103,7 @@ def cumsum(
         *args,
         **kwargs,
     ) -> Self:
+        validate_bool_kwarg(numeric_only, "numeric_only")
         data = self._get_numeric_data() if numeric_only else self
         return NDFrame.cumsum(data, axis, skipna, *args, **kwargs)
 
@@ -13109,6 +13116,7 @@ def cumprod(
         *args,
         **kwargs,
     ) -> Self:
+        validate_bool_kwarg(numeric_only, "numeric_only")
         data = self._get_numeric_data() if numeric_only else self
         return NDFrame.cumprod(data, axis, skipna, *args, **kwargs)
 
@@ -13227,6 +13235,7 @@ def idxmin(
         Beef                consumption
         dtype: object
         """
+        validate_bool_kwarg(numeric_only, "numeric_only")
         axis = self._get_axis_number(axis)
 
         if self.empty and len(self.axes[axis]):
@@ -13332,6 +13341,7 @@ def idxmax(
         Beef              co2_emissions
         dtype: object
         """
+        validate_bool_kwarg(numeric_only, "numeric_only")
         axis = self._get_axis_number(axis)
 
         if self.empty and len(self.axes[axis]):
@@ -13458,6 +13468,7 @@ def mode(
         spider   0.0  8.0
         ostrich  2.0  NaN
         """
+        validate_bool_kwarg(numeric_only, "numeric_only")
         data = self if not numeric_only else self._get_numeric_data()
 
         def f(s):
@@ -13596,6 +13607,7 @@ def quantile(
         """
         validate_percentile(q)
         axis = self._get_axis_number(axis)
+        validate_bool_kwarg(numeric_only, "numeric_only")
 
         if not is_list_like(q):
             # BlockManager.quantile expects listlike, so we wrap and unwrap here

pandas/core/generic.py

@@ -9271,6 +9271,7 @@ def rank(
         4    snake          NaN           NaN       NaN        5.0       NaN
         """
         axis_int = self._get_axis_number(axis)
+        validate_bool_kwarg(numeric_only, "numeric_only")
 
         if na_option not in {"keep", "top", "bottom"}:
             msg = "na_option must be one of 'keep', 'top', or 'bottom'"
@@ -11282,6 +11283,7 @@ def _logical_func(
     ) -> Series | bool:
         nv.validate_logical_func((), kwargs, fname=name)
         validate_bool_kwarg(skipna, "skipna", none_allowed=False)
+        validate_bool_kwarg(bool_only, "bool_only")
 
         if self.ndim > 1 and axis is None:
             # Reduce along one dimension then the other, to simplify DataFrame._reduce
@@ -11415,6 +11417,7 @@ def _stat_function_ddof(
     ) -> Series | float:
         nv.validate_stat_ddof_func((), kwargs, fname=name)
         validate_bool_kwarg(skipna, "skipna", none_allowed=False)
+        validate_bool_kwarg(numeric_only, "numeric_only")
 
         return self._reduce(
             func, name, axis=axis, numeric_only=numeric_only, skipna=skipna, ddof=ddof
@@ -11473,6 +11476,7 @@ def _stat_function(
         nv.validate_func(name, (), kwargs)
 
         validate_bool_kwarg(skipna, "skipna", none_allowed=False)
+        validate_bool_kwarg(numeric_only, "numeric_only")
 
         return self._reduce(
             func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only
@@ -11577,6 +11581,7 @@ def _min_count_stat_function(
         nv.validate_func(name, (), kwargs)
 
         validate_bool_kwarg(skipna, "skipna", none_allowed=False)
+        validate_bool_kwarg(numeric_only, "numeric_only")
 
         return self._reduce(
             func,

pandas/core/groupby/generic.py

@@ -38,6 +38,7 @@
     set_module,
 )
 from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import validate_bool_kwarg
 
 from pandas.core.dtypes.common import (
     ensure_int64,
@@ -157,6 +158,8 @@ def _wrap_agged_manager(self, mgr: Manager) -> Series:
     def _get_data_to_aggregate(
         self, *, numeric_only: bool = False, name: str | None = None
     ) -> SingleBlockManager:
+        validate_bool_kwarg(numeric_only, "numeric_only")
+
         ser = self._obj_with_exclusions
         single = ser._mgr
         if numeric_only and not is_numeric_dtype(ser.dtype):
@@ -683,6 +686,7 @@ def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
         )
 
     def _cython_transform(self, how: str, numeric_only: bool = False, **kwargs):
+        validate_bool_kwarg(numeric_only, "numeric_only")
         obj = self._obj_with_exclusions
 
         try:
@@ -2180,6 +2184,8 @@ def _cython_transform(
         # We have multi-block tests
         #  e.g. test_rank_min_int, test_cython_transform_frame
         #  test_transform_numeric_ret
+        validate_bool_kwarg(numeric_only, "numeric_only")
+
         mgr: BlockManager = self._get_data_to_aggregate(
             numeric_only=numeric_only, name=how
         )
@@ -2500,6 +2506,7 @@ def _gotitem(self, key, ndim: int, subset=None):
     def _get_data_to_aggregate(
         self, *, numeric_only: bool = False, name: str | None = None
     ) -> BlockManager:
+        validate_bool_kwarg(numeric_only, "numeric_only")
         obj = self._obj_with_exclusions
         mgr = obj._mgr
         if numeric_only:

pandas/core/groupby/groupby.py

@@ -70,6 +70,7 @@ class providing the base-class of operations.
     doc,
 )
 from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import validate_bool_kwarg
 
 from pandas.core.dtypes.cast import (
     coerce_indexer_dtype,
@@ -5676,6 +5677,7 @@ def _idxmax_idxmin(
         Series or DataFrame
             idxmax or idxmin for the groupby operation.
         """
+        validate_bool_kwarg(numeric_only, "numeric_only")
         if not self.observed and any(
             ping._passed_categorical for ping in self._grouper.groupings
         ):