Repository: pandas-dev/pandas (python)

Issue:
BUG: Series.plot(kind="pie") does not respect ylabel argument
### Pandas version checks

- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.

- [X] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.


### Reproducible Example

```python
ser = pd.Series([1, 2, 4], index=["a", "b", "c"], name="my_series")
ser.plot(kind="pie", ylabel=None)
```
```


### Issue Description

The plot will always show the label "my_series", regardless of if you pass ylabel=None or not.

The only way to fix this is to do:

```python
ax = ser.plot(kind="pie")
ax2.set_ylabel(None)
```

### Expected Behavior

ylabel argument should be respected

### Installed Versions

main

Pull request description:
BUG: Series.plot(kind="pie") does not respect ylabel argument
- [x] closes #58239
- [x] [Tests added and passed]
- [x] All [code checks passed]
- [x] Added [type annotations]
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.


Patch:
diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index 2b418d6655..58ecae66e1 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -98,9 +98,7 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.DataFrame.swaplevel SA01" \
         -i "pandas.DataFrame.to_markdown SA01" \
         -i "pandas.DataFrame.var PR01,RT03,SA01" \
-        -i "pandas.DatetimeIndex.indexer_at_time PR01,RT03" \
         -i "pandas.DatetimeIndex.snap PR01,RT03" \
-        -i "pandas.DatetimeIndex.to_period RT03" \
         -i "pandas.Grouper PR02" \
         -i "pandas.Index PR07" \
         -i "pandas.Index.append PR07,RT03,SA01" \
@@ -111,8 +109,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Index.get_indexer_non_unique PR07,SA01" \
         -i "pandas.Index.get_loc PR07,RT03,SA01" \
         -i "pandas.Index.identical PR01,SA01" \
-        -i "pandas.Index.insert PR07,RT03,SA01" \
-        -i "pandas.Index.intersection PR07,RT03,SA01" \
         -i "pandas.Index.join PR07,RT03,SA01" \
         -i "pandas.Index.names GL08" \
         -i "pandas.Index.nunique RT03" \
@@ -244,7 +240,7 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.dt.round PR01,PR02" \
         -i "pandas.Series.dt.seconds SA01" \
         -i "pandas.Series.dt.strftime PR01,PR02" \
-        -i "pandas.Series.dt.to_period PR01,PR02,RT03" \
+        -i "pandas.Series.dt.to_period PR01,PR02" \
         -i "pandas.Series.dt.total_seconds PR01" \
         -i "pandas.Series.dt.tz_convert PR01,PR02" \
         -i "pandas.Series.dt.tz_localize PR01,PR02" \
diff --git a/doc/source/whatsnew/v3.0.0.rst b/doc/source/whatsnew/v3.0.0.rst
index c77348b365..a81fb584c8 100644
--- a/doc/source/whatsnew/v3.0.0.rst
+++ b/doc/source/whatsnew/v3.0.0.rst
@@ -42,6 +42,7 @@ Other enhancements
 - :meth:`DataFrame.cummin`, :meth:`DataFrame.cummax`, :meth:`DataFrame.cumprod` and :meth:`DataFrame.cumsum` methods now have a ``numeric_only`` parameter (:issue:`53072`)
 - :meth:`DataFrame.fillna` and :meth:`Series.fillna` can now accept ``value=None``; for non-object dtype the corresponding NA value will be used (:issue:`57723`)
 - :meth:`Series.cummin` and :meth:`Series.cummax` now supports :class:`CategoricalDtype` (:issue:`52335`)
+- :meth:`Series.plot` now correctly handle the ``ylabel`` parameter for pie charts, allowing for explicit control over the y-axis label (:issue:`58239`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_300.notable_bug_fixes:
@@ -340,6 +341,9 @@ Performance improvements
 - Performance improvement in :meth:`RangeIndex.argmin` and :meth:`RangeIndex.argmax` (:issue:`57823`)
 - Performance improvement in :meth:`RangeIndex.insert` returning a :class:`RangeIndex` instead of a :class:`Index` when the :class:`RangeIndex` is empty. (:issue:`57833`)
 - Performance improvement in :meth:`RangeIndex.round` returning a :class:`RangeIndex` instead of a :class:`Index` when possible. (:issue:`57824`)
+- Performance improvement in :meth:`RangeIndex.searchsorted` (:issue:`58376`)
+- Performance improvement in :meth:`RangeIndex.to_numpy` when specifying an ``na_value`` (:issue:`58376`)
+- Performance improvement in :meth:`RangeIndex.value_counts` (:issue:`58376`)
 - Performance improvement in :meth:`RangeIndex.join` returning a :class:`RangeIndex` instead of a :class:`Index` when possible. (:issue:`57651`, :issue:`57752`)
 - Performance improvement in :meth:`RangeIndex.reindex` returning a :class:`RangeIndex` instead of a :class:`Index` when possible. (:issue:`57647`, :issue:`57752`)
 - Performance improvement in :meth:`RangeIndex.take` returning a :class:`RangeIndex` instead of a :class:`Index` when possible. (:issue:`57445`, :issue:`57752`)
diff --git a/pandas/_libs/lib.pyx b/pandas/_libs/lib.pyx
index 5b6d83ba8e..4fd68a1593 100644
--- a/pandas/_libs/lib.pyx
+++ b/pandas/_libs/lib.pyx
@@ -2808,14 +2808,14 @@ def maybe_convert_objects(ndarray[object] objects,
             from pandas.core.arrays import IntegerArray
 
             # Set these values to 1 to be deterministic, match
-            #  IntegerArray._internal_fill_value
+            #  IntegerDtype._internal_fill_value
             result[mask] = 1
             result = IntegerArray(result, mask)
         elif result is floats and convert_to_nullable_dtype:
             from pandas.core.arrays import FloatingArray
 
             # Set these values to 1.0 to be deterministic, match
-            #  FloatingArray._internal_fill_value
+            #  FloatingDtype._internal_fill_value
             result[mask] = 1.0
             result = FloatingArray(result, mask)
 
diff --git a/pandas/core/arrays/boolean.py b/pandas/core/arrays/boolean.py
index 813b10eef5..a326925545 100644
--- a/pandas/core/arrays/boolean.py
+++ b/pandas/core/arrays/boolean.py
@@ -68,6 +68,9 @@ class BooleanDtype(BaseMaskedDtype):
 
     name: ClassVar[str] = "boolean"
 
+    # The value used to fill '_data' to avoid upcasting
+    _internal_fill_value = False
+
     # https://github.com/python/mypy/issues/4125
     # error: Signature of "type" incompatible with supertype "BaseMaskedDtype"
     @property
@@ -293,13 +296,6 @@ class BooleanArray(BaseMaskedArray):
     Length: 3, dtype: boolean
     """
 
-    # The value used to fill '_data' to avoid upcasting
-    _internal_fill_value = False
-    # Fill values used for any/all
-    # Incompatible types in assignment (expression has type "bool", base class
-    # "BaseMaskedArray" defined the type as "<typing special form>")
-    _truthy_value = True  # type: ignore[assignment]
-    _falsey_value = False  # type: ignore[assignment]
     _TRUE_VALUES = {"True", "TRUE", "true", "1", "1.0"}
     _FALSE_VALUES = {"False", "FALSE", "false", "0", "0.0"}
 
diff --git a/pandas/core/arrays/datetimes.py b/pandas/core/arrays/datetimes.py
index b504897375..8747f795be 100644
--- a/pandas/core/arrays/datetimes.py
+++ b/pandas/core/arrays/datetimes.py
@@ -1207,6 +1207,7 @@ default 'raise'
         Returns
         -------
         PeriodArray/PeriodIndex
+            Immutable ndarray holding ordinal values at a particular frequency.
 
         Raises
         ------
diff --git a/pandas/core/arrays/floating.py b/pandas/core/arrays/floating.py
index 653e63e9d1..b3fbf0f92c 100644
--- a/pandas/core/arrays/floating.py
+++ b/pandas/core/arrays/floating.py
@@ -23,6 +23,8 @@ class FloatingDtype(NumericDtype):
     The attributes name & type are set when these subclasses are created.
     """
 
+    # The value used to fill '_data' to avoid upcasting
+    _internal_fill_value = np.nan
     _default_np_dtype = np.dtype(np.float64)
     _checker = is_float_dtype
 
@@ -113,14 +115,6 @@ class FloatingArray(NumericArray):
 
     _dtype_cls = FloatingDtype
 
-    # The value used to fill '_data' to avoid upcasting
-    _internal_fill_value = np.nan
-    # Fill values used for any/all
-    # Incompatible types in assignment (expression has type "float", base class
-    # "BaseMaskedArray" defined the type as "<typing special form>")
-    _truthy_value = 1.0  # type: ignore[assignment]
-    _falsey_value = 0.0  # type: ignore[assignment]
-
 
 _dtype_docstring = """
 An ExtensionDtype for {dtype} data.
diff --git a/pandas/core/arrays/integer.py b/pandas/core/arrays/integer.py
index dc453f3e37..21a9b09227 100644
--- a/pandas/core/arrays/integer.py
+++ b/pandas/core/arrays/integer.py
@@ -23,6 +23,8 @@ class IntegerDtype(NumericDtype):
     The attributes name & type are set when these subclasses are created.
     """
 
+    # The value used to fill '_data' to avoid upcasting
+    _internal_fill_value = 1
     _default_np_dtype = np.dtype(np.int64)
     _checker = is_integer_dtype
 
@@ -128,14 +130,6 @@ class IntegerArray(NumericArray):
 
     _dtype_cls = IntegerDtype
 
-    # The value used to fill '_data' to avoid upcasting
-    _internal_fill_value = 1
-    # Fill values used for any/all
-    # Incompatible types in assignment (expression has type "int", base class
-    # "BaseMaskedArray" defined the type as "<typing special form>")
-    _truthy_value = 1  # type: ignore[assignment]
-    _falsey_value = 0  # type: ignore[assignment]
-
 
 _dtype_docstring = """
 An ExtensionDtype for {dtype} integer data.
diff --git a/pandas/core/arrays/masked.py b/pandas/core/arrays/masked.py
index 190888d281..df794183f6 100644
--- a/pandas/core/arrays/masked.py
+++ b/pandas/core/arrays/masked.py
@@ -5,6 +5,7 @@ from typing import (
     Any,
     Callable,
     Literal,
+    cast,
     overload,
 )
 import warnings
@@ -16,22 +17,6 @@ from pandas._libs import (
     missing as libmissing,
 )
 from pandas._libs.tslibs import is_supported_dtype
-from pandas._typing import (
-    ArrayLike,
-    AstypeArg,
-    AxisInt,
-    DtypeObj,
-    FillnaOptions,
-    InterpolateOptions,
-    NpDtype,
-    PositionalIndexer,
-    Scalar,
-    ScalarIndexer,
-    Self,
-    SequenceIndexer,
-    Shape,
-    npt,
-)
 from pandas.compat import (
     IS64,
     is_platform_windows,
@@ -97,6 +82,20 @@ if TYPE_CHECKING:
     from pandas._typing import (
         NumpySorter,
         NumpyValueArrayLike,
+        ArrayLike,
+        AstypeArg,
+        AxisInt,
+        DtypeObj,
+        FillnaOptions,
+        InterpolateOptions,
+        NpDtype,
+        PositionalIndexer,
+        Scalar,
+        ScalarIndexer,
+        Self,
+        SequenceIndexer,
+        Shape,
+        npt,
     )
     from pandas._libs.missing import NAType
     from pandas.core.arrays import FloatingArray
@@ -111,16 +110,10 @@ class BaseMaskedArray(OpsMixin, ExtensionArray):
     numpy based
     """
 
-    # The value used to fill '_data' to avoid upcasting
-    _internal_fill_value: Scalar
     # our underlying data and mask are each ndarrays
     _data: np.ndarray
     _mask: npt.NDArray[np.bool_]
 
-    # Fill values used for any/all
-    _truthy_value = Scalar  # bool(_truthy_value) = True
-    _falsey_value = Scalar  # bool(_falsey_value) = False
-
     @classmethod
     def _simple_new(cls, values: np.ndarray, mask: npt.NDArray[np.bool_]) -> Self:
         result = BaseMaskedArray.__new__(cls)
@@ -155,8 +148,9 @@ class BaseMaskedArray(OpsMixin, ExtensionArray):
     @classmethod
     @doc(ExtensionArray._empty)
     def _empty(cls, shape: Shape, dtype: ExtensionDtype) -> Self:
-        values = np.empty(shape, dtype=dtype.type)
-        values.fill(cls._internal_fill_value)
+        dtype = cast(BaseMaskedDtype, dtype)
+        values: np.ndarray = np.empty(shape, dtype=dtype.type)
+        values.fill(dtype._internal_fill_value)
         mask = np.ones(shape, dtype=bool)
         result = cls(values, mask)
         if not isinstance(result, cls) or dtype != result.dtype:
@@ -917,7 +911,9 @@ class BaseMaskedArray(OpsMixin, ExtensionArray):
     ) -> Self:
         # we always fill with 1 internally
         # to avoid upcasting
-        data_fill_value = self._internal_fill_value if isna(fill_value) else fill_value
+        data_fill_value = (
+            self.dtype._internal_fill_value if isna(fill_value) else fill_value
+        )
         result = take(
             self._data,
             indexer,
@@ -1397,12 +1393,7 @@ class BaseMaskedArray(OpsMixin, ExtensionArray):
         nv.validate_any((), kwargs)
 
         values = self._data.copy()
-        # error: Argument 3 to "putmask" has incompatible type "object";
-        # expected "Union[_SupportsArray[dtype[Any]],
-        # _NestedSequence[_SupportsArray[dtype[Any]]],
-        # bool, int, float, complex, str, bytes,
-        # _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"
-        np.putmask(values, self._mask, self._falsey_value)  # type: ignore[arg-type]
+        np.putmask(values, self._mask, self.dtype._falsey_value)
         result = values.any()
         if skipna:
             return result
@@ -1490,12 +1481,7 @@ class BaseMaskedArray(OpsMixin, ExtensionArray):
         nv.validate_all((), kwargs)
 
         values = self._data.copy()
-        # error: Argument 3 to "putmask" has incompatible type "object";
-        # expected "Union[_SupportsArray[dtype[Any]],
-        # _NestedSequence[_SupportsArray[dtype[Any]]],
-        # bool, int, float, complex, str, bytes,
-        # _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"
-        np.putmask(values, self._mask, self._truthy_value)  # type: ignore[arg-type]
+        np.putmask(values, self._mask, self.dtype._truthy_value)
         result = values.all(axis=axis)
 
         if skipna:
diff --git a/pandas/core/arrays/numeric.py b/pandas/core/arrays/numeric.py
index fe7b32ec96..c5e9ed8698 100644
--- a/pandas/core/arrays/numeric.py
+++ b/pandas/core/arrays/numeric.py
@@ -221,7 +221,7 @@ def _coerce_to_data_and_mask(
     # we copy as need to coerce here
     if mask.any():
         values = values.copy()
-        values[mask] = cls._internal_fill_value
+        values[mask] = dtype_cls._internal_fill_value
     if inferred_type in ("string", "unicode"):
         # casts from str are always safe since they raise
         # a ValueError if the str cannot be parsed into a float
diff --git a/pandas/core/base.py b/pandas/core/base.py
index f535f0c554..e54fac3da7 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -556,7 +556,6 @@ class IndexOpsMixin(OpsMixin):
         """
         raise AbstractMethodError(self)
 
-    @final
     def to_numpy(
         self,
         dtype: npt.DTypeLike | None = None,
@@ -668,7 +667,7 @@ class IndexOpsMixin(OpsMixin):
         )
 
         values = self._values
-        if fillna:
+        if fillna and self.hasnans:
             if not can_hold_element(values, na_value):
                 # if we can't hold the na_value asarray either makes a copy or we
                 # error before modifying values. The asarray later on thus won't make
@@ -943,7 +942,6 @@ class IndexOpsMixin(OpsMixin):
 
         return algorithms.map_array(arr, mapper, na_action=na_action)
 
-    @final
     def value_counts(
         self,
         normalize: bool = False,
diff --git a/pandas/core/dtypes/dtypes.py b/pandas/core/dtypes/dtypes.py
index 778b6bd6f3..8c64a38bc1 100644
--- a/pandas/core/dtypes/dtypes.py
+++ b/pandas/core/dtypes/dtypes.py
@@ -79,6 +79,7 @@ if TYPE_CHECKING:
         DtypeObj,
         IntervalClosedType,
         Ordered,
+        Scalar,
         Self,
         npt,
         type_t,
@@ -1551,6 +1552,25 @@ class BaseMaskedDtype(ExtensionDtype):
 
     base = None
     type: type
+    _internal_fill_value: Scalar
+
+    @property
+    def _truthy_value(self):
+        # Fill values used for 'any'
+        if self.kind == "f":
+            return 1.0
+        if self.kind in "iu":
+            return 1
+        return True
+
+    @property
+    def _falsey_value(self):
+        # Fill values used for 'all'
+        if self.kind == "f":
+            return 0.0
+        if self.kind in "iu":
+            return 0
+        return False
 
     @property
     def na_value(self) -> libmissing.NAType:
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 8ea844d723..f0ac8604cc 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -3082,6 +3082,8 @@ class Index(IndexOpsMixin, PandasObject):
         Parameters
         ----------
         other : Index or array-like
+            An Index or an array-like object containing elements to form the
+            intersection with the original Index.
         sort : True, False or None, default False
             Whether to sort the resulting index.
 
@@ -3093,6 +3095,14 @@ class Index(IndexOpsMixin, PandasObject):
         Returns
         -------
         Index
+            Returns a new Index object with elements common to both the original Index
+            and the `other` Index.
+
+        See Also
+        --------
+        Index.union : Form the union of two Index objects.
+        Index.difference : Return a new Index with elements of index not in other.
+        Index.isin : Return a boolean array where the index values are in values.
 
         Examples
         --------
@@ -6625,11 +6635,19 @@ class Index(IndexOpsMixin, PandasObject):
         Parameters
         ----------
         loc : int
+            The integer location where the new item will be inserted.
         item : object
+            The new item to be inserted into the Index.
 
         Returns
         -------
         Index
+            Returns a new Index object resulting from inserting the specified item at
+            the specified location within the original Index.
+
+        See Also
+        --------
+        Index.append : Append a collection of Indexes together.
 
         Examples
         --------
diff --git a/pandas/core/indexes/datetimes.py b/pandas/core/indexes/datetimes.py
index 951455b627..742f66aa80 100644
--- a/pandas/core/indexes/datetimes.py
+++ b/pandas/core/indexes/datetimes.py
@@ -703,10 +703,13 @@ class DatetimeIndex(DatetimeTimedeltaMixin):
             Time passed in either as object (datetime.time) or as string in
             appropriate format ("%H:%M", "%H%M", "%I:%M%p", "%I%M%p",
             "%H:%M:%S", "%H%M%S", "%I:%M:%S%p", "%I%M%S%p").
+        asof : bool, default False
+            This parameter is currently not supported.
 
         Returns
         -------
         np.ndarray[np.intp]
+            Index locations of values at given `time` of day.
 
         See Also
         --------
diff --git a/pandas/core/indexes/range.py b/pandas/core/indexes/range.py
index 0ba3c22093..bd9e8b84fd 100644
--- a/pandas/core/indexes/range.py
+++ b/pandas/core/indexes/range.py
@@ -57,9 +57,13 @@ if TYPE_CHECKING:
         Dtype,
         JoinHow,
         NaPosition,
+        NumpySorter,
         Self,
         npt,
     )
+
+    from pandas import Series
+
 _empty_range = range(0)
 _dtype_int64 = np.dtype(np.int64)
 
@@ -1359,3 +1363,64 @@ class RangeIndex(Index):
                 taken += self.start
 
         return self._shallow_copy(taken, name=self.name)
+
+    def value_counts(
+        self,
+        normalize: bool = False,
+        sort: bool = True,
+        ascending: bool = False,
+        bins=None,
+        dropna: bool = True,
+    ) -> Series:
+        from pandas import Series
+
+        if bins is not None:
+            return super().value_counts(
+                normalize=normalize,
+                sort=sort,
+                ascending=ascending,
+                bins=bins,
+                dropna=dropna,
+            )
+        name = "proportion" if normalize else "count"
+        data: npt.NDArray[np.floating] | npt.NDArray[np.signedinteger] = np.ones(
+            len(self), dtype=np.int64
+        )
+        if normalize:
+            data = data / len(self)
+        return Series(data, index=self.copy(), name=name)
+
+    def searchsorted(  # type: ignore[override]
+        self,
+        value,
+        side: Literal["left", "right"] = "left",
+        sorter: NumpySorter | None = None,
+    ) -> npt.NDArray[np.intp] | np.intp:
+        if side not in {"left", "right"} or sorter is not None:
+            return super().searchsorted(value=value, side=side, sorter=sorter)
+
+        was_scalar = False
+        if is_scalar(value):
+            was_scalar = True
+            array_value = np.array([value])
+        else:
+            array_value = np.asarray(value)
+        if array_value.dtype.kind not in "iu":
+            return super().searchsorted(value=value, side=side, sorter=sorter)
+
+        if flip := (self.step < 0):
+            rng = self._range[::-1]
+            start = rng.start
+            step = rng.step
+            shift = side == "right"
+        else:
+            start = self.start
+            step = self.step
+            shift = side == "left"
+        result = (array_value - start - int(shift)) // step + 1
+        if flip:
+            result = len(self) - result
+        result = np.maximum(np.minimum(result, len(self)), 0)
+        if was_scalar:
+            return np.intp(result.item())
+        return result.astype(np.intp, copy=False)
diff --git a/pandas/plotting/_matplotlib/core.py b/pandas/plotting/_matplotlib/core.py
index 38a75e741d..fffeb9b824 100644
--- a/pandas/plotting/_matplotlib/core.py
+++ b/pandas/plotting/_matplotlib/core.py
@@ -2077,9 +2077,6 @@ class PiePlot(MPLPlot):
 
         for i, (label, y) in enumerate(self._iter_data(data=self.data)):
             ax = self._get_ax(i)
-            if label is not None:
-                label = pprint_thing(label)
-                ax.set_ylabel(label)
 
             kwds = self.kwds.copy()
 


Test patch:
diff --git a/pandas/tests/indexes/ranges/test_range.py b/pandas/tests/indexes/ranges/test_range.py
index 727edb7ae3..1f9df30d60 100644
--- a/pandas/tests/indexes/ranges/test_range.py
+++ b/pandas/tests/indexes/ranges/test_range.py
@@ -874,3 +874,36 @@ def test_getitem_integers_return_index():
     result = RangeIndex(0, 10, 2, name="foo")[[0, 1, -1]]
     expected = Index([0, 2, 8], dtype="int64", name="foo")
     tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("normalize", [True, False])
+@pytest.mark.parametrize(
+    "rng",
+    [
+        range(3),
+        range(0),
+        range(0, 3, 2),
+        range(3, -3, -2),
+    ],
+)
+def test_value_counts(sort, dropna, ascending, normalize, rng):
+    ri = RangeIndex(rng, name="A")
+    result = ri.value_counts(
+        normalize=normalize, sort=sort, ascending=ascending, dropna=dropna
+    )
+    expected = Index(list(rng), name="A").value_counts(
+        normalize=normalize, sort=sort, ascending=ascending, dropna=dropna
+    )
+    tm.assert_series_equal(result, expected, check_index_type=False)
+
+
+@pytest.mark.parametrize("side", ["left", "right"])
+@pytest.mark.parametrize("value", [0, -5, 5, -3, np.array([-5, -3, 0, 5])])
+def test_searchsorted(side, value):
+    ri = RangeIndex(-3, 3, 2)
+    result = ri.searchsorted(value=value, side=side)
+    expected = Index(list(ri)).searchsorted(value=value, side=side)
+    if isinstance(value, int):
+        assert result == expected
+    else:
+        tm.assert_numpy_array_equal(result, expected)
diff --git a/pandas/tests/plotting/frame/test_frame.py b/pandas/tests/plotting/frame/test_frame.py
index c30cb96fef..adb56a40b0 100644
--- a/pandas/tests/plotting/frame/test_frame.py
+++ b/pandas/tests/plotting/frame/test_frame.py
@@ -1629,7 +1629,7 @@ class TestDataFramePlots:
         for ax in axes:
             _check_text_labels(ax.texts, df.index)
         for ax, ylabel in zip(axes, df.columns):
-            assert ax.get_ylabel() == ylabel
+            assert ax.get_ylabel() == ""
 
     def test_pie_df_labels_colors(self):
         df = DataFrame(
diff --git a/pandas/tests/plotting/test_series.py b/pandas/tests/plotting/test_series.py
index 9fbc20e10f..54f09c7007 100644
--- a/pandas/tests/plotting/test_series.py
+++ b/pandas/tests/plotting/test_series.py
@@ -378,7 +378,7 @@ class TestSeriesPlots:
         )
         ax = _check_plot_works(series.plot.pie)
         _check_text_labels(ax.texts, series.index)
-        assert ax.get_ylabel() == "YLABEL"
+        assert ax.get_ylabel() == ""
 
     def test_pie_series_no_label(self):
         series = Series(