diff --git a/pandas/core/arrays/datetimes.py b/pandas/core/arrays/datetimes.py
index 32d6279250fe2..1be2ec0dd92d7 100644
--- a/pandas/core/arrays/datetimes.py
+++ b/pandas/core/arrays/datetimes.py
@@ -190,7 +190,7 @@ class DatetimeArray(dtl.TimelikeOps, dtl.DatelikeOps):
     _infer_matches = ("datetime", "datetime64", "date")
 
     # define my properties & methods for delegation
-    _bool_ops = [
+    _bool_ops: list[str] = [
         "is_month_start",
         "is_month_end",
         "is_quarter_start",
@@ -199,8 +199,8 @@ class DatetimeArray(dtl.TimelikeOps, dtl.DatelikeOps):
         "is_year_end",
         "is_leap_year",
     ]
-    _object_ops = ["freq", "tz"]
-    _field_ops = [
+    _object_ops: list[str] = ["freq", "tz"]
+    _field_ops: list[str] = [
         "year",
         "month",
         "day",
@@ -220,9 +220,9 @@ class DatetimeArray(dtl.TimelikeOps, dtl.DatelikeOps):
         "microsecond",
         "nanosecond",
     ]
-    _other_ops = ["date", "time", "timetz"]
-    _datetimelike_ops = _field_ops + _object_ops + _bool_ops + _other_ops
-    _datetimelike_methods = [
+    _other_ops: list[str] = ["date", "time", "timetz"]
+    _datetimelike_ops: list[str] = _field_ops + _object_ops + _bool_ops + _other_ops
+    _datetimelike_methods: list[str] = [
         "to_period",
         "tz_localize",
         "tz_convert",
diff --git a/pandas/core/arrays/interval.py b/pandas/core/arrays/interval.py
index c256cc2e6a368..8d3a8feb89d67 100644
--- a/pandas/core/arrays/interval.py
+++ b/pandas/core/arrays/interval.py
@@ -87,7 +87,7 @@
 
 IntervalArrayT = TypeVar("IntervalArrayT", bound="IntervalArray")
 
-_interval_shared_docs = {}
+_interval_shared_docs: dict[str, str] = {}
 
 _shared_docs_kwargs = {
     "klass": "IntervalArray",
diff --git a/pandas/core/arrays/period.py b/pandas/core/arrays/period.py
index 7234772466bd5..62829efb9dd8a 100644
--- a/pandas/core/arrays/period.py
+++ b/pandas/core/arrays/period.py
@@ -157,9 +157,9 @@ class PeriodArray(PeriodMixin, dtl.DatelikeOps):
 
     # Names others delegate to us
     _other_ops: list[str] = []
-    _bool_ops = ["is_leap_year"]
-    _object_ops = ["start_time", "end_time", "freq"]
-    _field_ops = [
+    _bool_ops: list[str] = ["is_leap_year"]
+    _object_ops: list[str] = ["start_time", "end_time", "freq"]
+    _field_ops: list[str] = [
         "year",
         "month",
         "day",
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 2f1e8fa9a80a7..5e96eecd68604 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -1797,9 +1797,9 @@ def _drop_labels_or_levels(self, keys, axis: int = 0):
 
         Parameters
         ----------
-        keys: str or list of str
+        keys : str or list of str
             labels or levels to drop
-        axis: int, default 0
+        axis : int, default 0
             Axis that levels are associated with (0 for index, 1 for columns)
 
         Returns
diff --git a/pandas/core/groupby/base.py b/pandas/core/groupby/base.py
index 927eb8eed8454..50248d5af8883 100644
--- a/pandas/core/groupby/base.py
+++ b/pandas/core/groupby/base.py
@@ -3,8 +3,9 @@
 hold the allowlist of methods that are exposed on the
 SeriesGroupBy and the DataFrameGroupBy objects.
 """
+from __future__ import annotations
+
 import collections
-from typing import List
 
 from pandas._typing import final
 
@@ -19,7 +20,7 @@
 
 
 class ShallowMixin(PandasObject):
-    _attributes: List[str] = []
+    _attributes: list[str] = []
 
     @final
     def _shallow_copy(self, obj, **kwargs):
@@ -39,7 +40,7 @@ class GotItemMixin(PandasObject):
     Provide the groupby facilities to the mixed object.
     """
 
-    _attributes: List[str]
+    _attributes: list[str]
 
     @final
     def _gotitem(self, key, ndim, subset=None):
@@ -106,12 +107,16 @@ def _gotitem(self, key, ndim, subset=None):
     | plotting_methods
 )
 
-series_apply_allowlist = (
+series_apply_allowlist: frozenset[str] = (
     common_apply_allowlist
-    | {"nlargest", "nsmallest", "is_monotonic_increasing", "is_monotonic_decreasing"}
+    | frozenset(
+        {"nlargest", "nsmallest", "is_monotonic_increasing", "is_monotonic_decreasing"}
+    )
 ) | frozenset(["dtype", "unique"])
 
-dataframe_apply_allowlist = common_apply_allowlist | frozenset(["dtypes", "corrwith"])
+dataframe_apply_allowlist: frozenset[str] = common_apply_allowlist | frozenset(
+    ["dtypes", "corrwith"]
+)
 
 # cythonized transformations or canned "agg+broadcast", which do not
 # require postprocessing of the result by transform.
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index a0727500ecc81..77770f6c79c8b 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -173,7 +173,7 @@
 
 _unsortable_types = frozenset(("mixed", "mixed-integer"))
 
-_index_doc_kwargs = {
+_index_doc_kwargs: dict[str, str] = {
     "klass": "Index",
     "inplace": "",
     "target_klass": "Index",
@@ -181,7 +181,7 @@
     "unique": "Index",
     "duplicated": "np.ndarray",
 }
-_index_shared_docs = {}
+_index_shared_docs: dict[str, str] = {}
 str_t = str
 
 
@@ -1189,7 +1189,7 @@ def _format_with_header(
         return header + result
 
     @final
-    def to_native_types(self, slicer=None, **kwargs):
+    def to_native_types(self, slicer=None, **kwargs) -> np.ndarray:
         """
         Format specified values of `self` and return them.
 
@@ -4390,7 +4390,7 @@ def memory_usage(self, deep: bool = False) -> int:
         return result
 
     @final
-    def where(self, cond, other=None):
+    def where(self, cond, other=None) -> Index:
         """
         Replace values where the condition is False.
 
@@ -4606,7 +4606,7 @@ def _can_hold_identifiers_and_holds_name(self, name) -> bool:
             return name in self
         return False
 
-    def append(self, other):
+    def append(self, other) -> Index:
         """
         Append a collection of Index options together.
 
@@ -4616,7 +4616,7 @@ def append(self, other):
 
         Returns
         -------
-        appended : Index
+        Index
         """
         to_concat = [self]
 
@@ -4846,7 +4846,7 @@ def asof(self, label):
                 loc = loc.indices(len(self))[-1]
             return self[loc]
 
-    def asof_locs(self, where: Index, mask) -> np.ndarray:
+    def asof_locs(self, where: Index, mask: np.ndarray) -> np.ndarray:
         """
         Return the locations (indices) of labels in the index.
 
@@ -4863,13 +4863,13 @@ def asof_locs(self, where: Index, mask) -> np.ndarray:
         ----------
         where : Index
             An Index consisting of an array of timestamps.
-        mask : array-like
+        mask : np.ndarray[bool]
             Array of booleans denoting where values in the original
             data are not NA.
 
         Returns
         -------
-        numpy.ndarray
+        np.ndarray[np.intp]
             An array of locations (indices) of the labels from the Index
             which correspond to the return values of the `asof` function
             for every element in `where`.
@@ -4877,7 +4877,7 @@ def asof_locs(self, where: Index, mask) -> np.ndarray:
         locs = self._values[mask].searchsorted(where._values, side="right")
         locs = np.where(locs > 0, locs - 1, 0)
 
-        result = np.arange(len(self))[mask].take(locs)
+        result = np.arange(len(self), dtype=np.intp)[mask].take(locs)
 
         # TODO: overload return type of ExtensionArray.__getitem__
         first_value = cast(Any, self._values[mask.argmax()])
@@ -5050,7 +5050,7 @@ def argsort(self, *args, **kwargs) -> np.ndarray:
 
         Returns
         -------
-        numpy.ndarray
+        np.ndarray[np.intp]
             Integer indices that would sort the index if used as
             an indexer.
 
@@ -5838,7 +5838,7 @@ def delete(self, loc) -> Index:
         Returns
         -------
         Index
-            New Index with passed location(-s) deleted.
+            Will be same type as self, except for RangeIndex.
 
         See Also
         --------
@@ -6352,8 +6352,8 @@ def _maybe_cast_data_without_dtype(subarr):
 
     elif inferred == "interval":
         try:
-            data = IntervalArray._from_sequence(subarr, copy=False)
-            return data
+            ia_data = IntervalArray._from_sequence(subarr, copy=False)
+            return ia_data
         except (ValueError, TypeError):
             # GH27172: mixed closed Intervals --> object dtype
             pass
diff --git a/pandas/core/indexes/category.py b/pandas/core/indexes/category.py
index c9c39fde1da46..b5089621313b8 100644
--- a/pandas/core/indexes/category.py
+++ b/pandas/core/indexes/category.py
@@ -1,8 +1,8 @@
+from __future__ import annotations
+
 from typing import (
     Any,
     Hashable,
-    List,
-    Optional,
 )
 import warnings
 
@@ -50,7 +50,7 @@
     inherit_names,
 )
 
-_index_doc_kwargs = dict(ibase._index_doc_kwargs)
+_index_doc_kwargs: dict[str, str] = dict(ibase._index_doc_kwargs)
 _index_doc_kwargs.update({"target_klass": "CategoricalIndex"})
 
 
@@ -216,10 +216,10 @@ def __new__(
         data=None,
         categories=None,
         ordered=None,
-        dtype: Optional[Dtype] = None,
-        copy=False,
-        name=None,
-    ):
+        dtype: Dtype | None = None,
+        copy: bool = False,
+        name: Hashable = None,
+    ) -> CategoricalIndex:
 
         name = maybe_extract_name(name, data, cls)
 
@@ -239,7 +239,7 @@ def _shallow_copy(
         self,
         values: Categorical,
         name: Hashable = no_default,
-    ):
+    ) -> CategoricalIndex:
         name = self._name if name is no_default else name
 
         if values is not None:
@@ -349,7 +349,7 @@ def _format_attrs(self):
             attrs.append(("length", len(self)))
         return attrs
 
-    def _format_with_header(self, header: List[str], na_rep: str = "NaN") -> List[str]:
+    def _format_with_header(self, header: list[str], na_rep: str = "NaN") -> list[str]:
         from pandas.io.formats.printing import pprint_thing
 
         result = [
@@ -422,10 +422,9 @@ def reindex(self, target, method=None, level=None, limit=None, tolerance=None):
 
         target = ibase.ensure_index(target)
 
-        missing: List[int]
         if self.equals(target):
             indexer = None
-            missing = []
+            missing = np.array([], dtype=np.intp)
         else:
             indexer, missing = self.get_indexer_non_unique(np.array(target))
 
@@ -494,8 +493,8 @@ def _maybe_cast_indexer(self, key) -> int:
     def _get_indexer(
         self,
         target: Index,
-        method: Optional[str] = None,
-        limit: Optional[int] = None,
+        method: str | None = None,
+        limit: int | None = None,
         tolerance=None,
     ) -> np.ndarray:
 
@@ -626,7 +625,7 @@ def map(self, mapper):
         mapped = self._values.map(mapper)
         return Index(mapped, name=self.name)
 
-    def _concat(self, to_concat: List[Index], name: Hashable) -> Index:
+    def _concat(self, to_concat: list[Index], name: Hashable) -> Index:
         # if calling index is category, don't check dtype of others
         try:
             codes = np.concatenate([self._is_dtype_compat(c).codes for c in to_concat])
diff --git a/pandas/core/indexes/datetimes.py b/pandas/core/indexes/datetimes.py
index b3714ff004978..9f02196466ebf 100644
--- a/pandas/core/indexes/datetimes.py
+++ b/pandas/core/indexes/datetimes.py
@@ -8,7 +8,10 @@
     tzinfo,
 )
 import operator
-from typing import TYPE_CHECKING
+from typing import (
+    TYPE_CHECKING,
+    Hashable,
+)
 import warnings
 
 import numpy as np
@@ -314,15 +317,15 @@ def __new__(
         data=None,
         freq=lib.no_default,
         tz=None,
-        normalize=False,
+        normalize: bool = False,
         closed=None,
         ambiguous="raise",
-        dayfirst=False,
-        yearfirst=False,
+        dayfirst: bool = False,
+        yearfirst: bool = False,
         dtype: Dtype | None = None,
-        copy=False,
-        name=None,
-    ):
+        copy: bool = False,
+        name: Hashable = None,
+    ) -> DatetimeIndex:
 
         if is_scalar(data):
             raise TypeError(
@@ -637,7 +640,7 @@ def _validate_partial_date_slice(self, reso: Resolution):
             # _parsed_string_to_bounds allows it.
             raise KeyError
 
-    def _deprecate_mismatched_indexing(self, key):
+    def _deprecate_mismatched_indexing(self, key) -> None:
         # GH#36148
         # we get here with isinstance(key, self._data._recognized_scalars)
         try:
@@ -851,7 +854,7 @@ def inferred_type(self) -> str:
         # sure we can't have ambiguous indexing
         return "datetime64"
 
-    def indexer_at_time(self, time, asof=False):
+    def indexer_at_time(self, time, asof: bool = False) -> np.ndarray:
         """
         Return index locations of values at particular time of day
         (e.g. 9:30AM).
@@ -865,7 +868,7 @@ def indexer_at_time(self, time, asof=False):
 
         Returns
         -------
-        values_at_time : array of integers
+        np.ndarray[np.intp]
 
         See Also
         --------
@@ -891,8 +894,8 @@ def indexer_at_time(self, time, asof=False):
         return (micros == time_micros).nonzero()[0]
 
     def indexer_between_time(
-        self, start_time, end_time, include_start=True, include_end=True
-    ):
+        self, start_time, end_time, include_start: bool = True, include_end: bool = True
+    ) -> np.ndarray:
         """
         Return index locations of values between particular times of day
         (e.g., 9:00-9:30AM).
@@ -908,7 +911,7 @@ def indexer_between_time(
 
         Returns
         -------
-        values_between_time : array of integers
+        np.ndarray[np.intp]
 
         See Also
         --------
@@ -948,8 +951,8 @@ def date_range(
     periods=None,
     freq=None,
     tz=None,
-    normalize=False,
-    name=None,
+    normalize: bool = False,
+    name: Hashable = None,
     closed=None,
     **kwargs,
 ) -> DatetimeIndex:
@@ -1120,8 +1123,8 @@ def bdate_range(
     periods: int | None = None,
     freq="B",
     tz=None,
-    normalize=True,
-    name=None,
+    normalize: bool = True,
+    name: Hashable = None,
     weekmask=None,
     holidays=None,
     closed=None,
diff --git a/pandas/core/indexes/extension.py b/pandas/core/indexes/extension.py
index 76c16dee1cda1..b11ec06120e0c 100644
--- a/pandas/core/indexes/extension.py
+++ b/pandas/core/indexes/extension.py
@@ -351,7 +351,7 @@ def map(self, mapper, na_action=None):
             return self.astype(object).map(mapper)
 
     @doc(Index.astype)
-    def astype(self, dtype, copy=True):
+    def astype(self, dtype, copy: bool = True) -> Index:
         dtype = pandas_dtype(dtype)
         if is_dtype_equal(self.dtype, dtype):
             if not copy:
@@ -410,7 +410,7 @@ def _simple_new(
     def _get_engine_target(self) -> np.ndarray:
         return self._data._ndarray
 
-    def insert(self: _T, loc: int, item) -> _T:
+    def insert(self: _T, loc: int, item) -> Index:
         """
         Make new Index inserting new item at location. Follows
         Python list.append semantics for negative values.
diff --git a/pandas/core/indexes/interval.py b/pandas/core/indexes/interval.py
index cd85efaba1d33..f7ab09e4f176f 100644
--- a/pandas/core/indexes/interval.py
+++ b/pandas/core/indexes/interval.py
@@ -283,7 +283,7 @@ def __new__(
         copy: bool = False,
         name: Hashable = None,
         verify_integrity: bool = True,
-    ):
+    ) -> IntervalIndex:
 
         name = maybe_extract_name(name, data, cls)
 
@@ -318,10 +318,10 @@ def from_breaks(
         cls,
         breaks,
         closed: str = "right",
-        name=None,
+        name: Hashable = None,
         copy: bool = False,
         dtype: Dtype | None = None,
-    ):
+    ) -> IntervalIndex:
         with rewrite_exception("IntervalArray", cls.__name__):
             array = IntervalArray.from_breaks(
                 breaks, closed=closed, copy=copy, dtype=dtype
@@ -349,10 +349,10 @@ def from_arrays(
         left,
         right,
         closed: str = "right",
-        name=None,
+        name: Hashable = None,
         copy: bool = False,
         dtype: Dtype | None = None,
-    ):
+    ) -> IntervalIndex:
         with rewrite_exception("IntervalArray", cls.__name__):
             array = IntervalArray.from_arrays(
                 left, right, closed, copy=copy, dtype=dtype
@@ -379,10 +379,10 @@ def from_tuples(
         cls,
         data,
         closed: str = "right",
-        name=None,
+        name: Hashable = None,
         copy: bool = False,
         dtype: Dtype | None = None,
-    ):
+    ) -> IntervalIndex:
         with rewrite_exception("IntervalArray", cls.__name__):
             arr = IntervalArray.from_tuples(data, closed=closed, copy=copy, dtype=dtype)
         return cls._simple_new(arr, name=name)
@@ -878,7 +878,7 @@ def putmask(self, mask, value) -> Index:
         arr.putmask(mask, value)
         return type(self)._simple_new(arr, name=self.name)
 
-    def insert(self, loc, item):
+    def insert(self, loc: int, item):
         """
         Return a new IntervalIndex inserting new item at location. Follows
         Python list.append semantics for negative values.  Only Interval
@@ -1077,8 +1077,8 @@ def _is_type_compatible(a, b) -> bool:
 
 
 def interval_range(
-    start=None, end=None, periods=None, freq=None, name=None, closed="right"
-):
+    start=None, end=None, periods=None, freq=None, name: Hashable = None, closed="right"
+) -> IntervalIndex:
     """
     Return a fixed frequency IntervalIndex.
 
diff --git a/pandas/core/indexes/period.py b/pandas/core/indexes/period.py
index a32585c3bed13..18e441ef165c9 100644
--- a/pandas/core/indexes/period.py
+++ b/pandas/core/indexes/period.py
@@ -4,7 +4,10 @@
     datetime,
     timedelta,
 )
-from typing import Any
+from typing import (
+    Any,
+    Hashable,
+)
 import warnings
 
 import numpy as np
@@ -215,10 +218,10 @@ def __new__(
         ordinal=None,
         freq=None,
         dtype: Dtype | None = None,
-        copy=False,
-        name=None,
+        copy: bool = False,
+        name: Hashable = None,
         **fields,
-    ):
+    ) -> PeriodIndex:
 
         valid_field_set = {
             "year",
@@ -322,7 +325,7 @@ def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
     # ------------------------------------------------------------------------
     # Rendering Methods
 
-    def _mpl_repr(self):
+    def _mpl_repr(self) -> np.ndarray:
         # how to represent ourselves to matplotlib
         return self.astype(object)._values
 
@@ -386,7 +389,8 @@ def __array_wrap__(self, result, context=None):
     def asof_locs(self, where: Index, mask: np.ndarray) -> np.ndarray:
         """
         where : array of timestamps
-        mask : array of booleans where data is not NA
+        mask : np.ndarray[bool]
+            Array of booleans where data is not NA.
         """
         if isinstance(where, DatetimeIndex):
             where = PeriodIndex(where._values, freq=self.freq)
diff --git a/pandas/core/indexes/range.py b/pandas/core/indexes/range.py
index 56bd11056b380..8e8c67927c20f 100644
--- a/pandas/core/indexes/range.py
+++ b/pandas/core/indexes/range.py
@@ -106,9 +106,9 @@ def __new__(
         stop=None,
         step=None,
         dtype: Dtype | None = None,
-        copy=False,
-        name=None,
-    ):
+        copy: bool = False,
+        name: Hashable = None,
+    ) -> RangeIndex:
 
         # error: Argument 1 to "_validate_dtype" of "NumericIndex" has incompatible type
         # "Union[ExtensionDtype, str, dtype[Any], Type[str], Type[float], Type[int],
@@ -584,7 +584,7 @@ def _intersection(self, other: Index, sort=False):
         # solve intersection problem
         # performance hint: for identical step sizes, could use
         # cheaper alternative
-        gcd, s, t = self._extended_gcd(first.step, second.step)
+        gcd, s, _ = self._extended_gcd(first.step, second.step)
 
         # check whether element sets intersect
         if (first.start - second.start) % gcd:
@@ -619,7 +619,7 @@ def _max_fitting_element(self, upper_limit: int) -> int:
         no_steps = (upper_limit - self.start) // abs(self.step)
         return self.start + abs(self.step) * no_steps
 
-    def _extended_gcd(self, a, b):
+    def _extended_gcd(self, a: int, b: int) -> tuple[int, int, int]:
         """
         Extended Euclidean algorithms to solve Bezout's identity:
            a*x + b*y = gcd(x, y)
@@ -745,7 +745,7 @@ def _difference(self, other, sort=None):
             new_index = new_index[::-1]
         return new_index
 
-    def symmetric_difference(self, other, result_name=None, sort=None):
+    def symmetric_difference(self, other, result_name: Hashable = None, sort=None):
         if not isinstance(other, RangeIndex) or sort is not None:
             return super().symmetric_difference(other, result_name, sort)
 
@@ -759,7 +759,7 @@ def symmetric_difference(self, other, result_name=None, sort=None):
 
     # --------------------------------------------------------------------
 
-    def _concat(self, indexes, name):
+    def _concat(self, indexes, name: Hashable):
         """
         Overriding parent method for the case of all RangeIndex instances.
 
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index e1ba532e6a5f1..fb542711329b4 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -1066,7 +1066,7 @@ def _getitem_iterable(self, key, axis: int):
         ----------
         key : iterable
             Targeted labels.
-        axis: int
+        axis : int
             Dimension on which the indexing is being made.
 
         Raises
@@ -1266,7 +1266,7 @@ def _get_listlike_indexer(self, key, axis: int, raise_missing: bool = False):
         ----------
         key : list-like
             Targeted labels.
-        axis: int
+        axis:  int
             Dimension on which the indexing is being made.
         raise_missing: bool, default False
             Whether to raise a KeyError if some labels were not found.
@@ -1321,7 +1321,7 @@ def _validate_read_indexer(
         indexer: array-like of booleans
             Indices corresponding to the key,
             (with -1 indicating not found).
-        axis: int
+        axis : int
             Dimension on which the indexing is being made.
         raise_missing: bool
             Whether to raise a KeyError if some labels are not found. Will be
diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index 43a61ed799e8f..54588eafc3fa0 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -504,7 +504,7 @@ def nanall(
     Parameters
     ----------
     values : ndarray
-    axis: int, optional
+    axis : int, optional
     skipna : bool, default True
     mask : ndarray[bool], optional
         nan-mask if known
@@ -547,7 +547,7 @@ def nansum(
     Parameters
     ----------
     values : ndarray[dtype]
-    axis: int, optional
+    axis : int, optional
     skipna : bool, default True
     min_count: int, default 0
     mask : ndarray[bool], optional
@@ -622,7 +622,7 @@ def nanmean(
     Parameters
     ----------
     values : ndarray
-    axis: int, optional
+    axis : int, optional
     skipna : bool, default True
     mask : ndarray[bool], optional
         nan-mask if known
@@ -678,7 +678,7 @@ def nanmedian(values, *, axis=None, skipna=True, mask=None):
     Parameters
     ----------
     values : ndarray
-    axis: int, optional
+    axis : int, optional
     skipna : bool, default True
     mask : ndarray[bool], optional
         nan-mask if known
@@ -835,7 +835,7 @@ def nanstd(values, *, axis=None, skipna=True, ddof=1, mask=None):
     Parameters
     ----------
     values : ndarray
-    axis: int, optional
+    axis : int, optional
     skipna : bool, default True
     ddof : int, default 1
         Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
@@ -875,7 +875,7 @@ def nanvar(values, *, axis=None, skipna=True, ddof=1, mask=None):
     Parameters
     ----------
     values : ndarray
-    axis: int, optional
+    axis : int, optional
     skipna : bool, default True
     ddof : int, default 1
         Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
@@ -950,7 +950,7 @@ def nansem(
     Parameters
     ----------
     values : ndarray
-    axis: int, optional
+    axis : int, optional
     skipna : bool, default True
     ddof : int, default 1
         Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
@@ -1031,7 +1031,7 @@ def nanargmax(
     Parameters
     ----------
     values : ndarray
-    axis: int, optional
+    axis : int, optional
     skipna : bool, default True
     mask : ndarray[bool], optional
         nan-mask if known
@@ -1077,7 +1077,7 @@ def nanargmin(
     Parameters
     ----------
     values : ndarray
-    axis: int, optional
+    axis : int, optional
     skipna : bool, default True
     mask : ndarray[bool], optional
         nan-mask if known
@@ -1129,7 +1129,7 @@ def nanskew(
     Parameters
     ----------
     values : ndarray
-    axis: int, optional
+    axis : int, optional
     skipna : bool, default True
     mask : ndarray[bool], optional
         nan-mask if known
@@ -1216,7 +1216,7 @@ def nankurt(
     Parameters
     ----------
     values : ndarray
-    axis: int, optional
+    axis : int, optional
     skipna : bool, default True
     mask : ndarray[bool], optional
         nan-mask if known
@@ -1307,7 +1307,7 @@ def nanprod(
     Parameters
     ----------
     values : ndarray[dtype]
-    axis: int, optional
+    axis : int, optional
     skipna : bool, default True
     min_count: int, default 0
     mask : ndarray[bool], optional
diff --git a/pandas/core/ops/__init__.py b/pandas/core/ops/__init__.py
index 689f27a25f11b..f6bde348888a1 100644
--- a/pandas/core/ops/__init__.py
+++ b/pandas/core/ops/__init__.py
@@ -212,8 +212,8 @@ def align_method_FRAME(
     ----------
     left : DataFrame
     right : Any
-    axis: int, str, or None
-    flex: bool or None, default False
+    axis : int, str, or None
+    flex : bool or None, default False
         Whether this is a flex op, in which case we reindex.
         None indicates not to check for alignment.
     level : int or level name, default None
diff --git a/pandas/core/sorting.py b/pandas/core/sorting.py
index ec7f66f1e515e..e2ff09705f4cb 100644
--- a/pandas/core/sorting.py
+++ b/pandas/core/sorting.py
@@ -409,7 +409,7 @@ def nargminmax(values, method: str, axis: int = 0):
     ----------
     values : ExtensionArray
     method : {"argmax", "argmin"}
-    axis: int, default 0
+    axis : int, default 0
 
     Returns
     -------
diff --git a/pandas/tests/reshape/concat/test_append.py b/pandas/tests/reshape/concat/test_append.py
index 1c533ec9fb1e3..62fe1ed3a7c49 100644
--- a/pandas/tests/reshape/concat/test_append.py
+++ b/pandas/tests/reshape/concat/test_append.py
@@ -190,7 +190,10 @@ def test_append_preserve_index_name(self):
         pd.MultiIndex.from_arrays(["A B C".split(), "D E F".split()])
     ]
 
-    all_indexes = indexes_can_append + indexes_cannot_append_with_other
+    # error: Unsupported operand types for + ("List[Index]" and "List[MultiIndex]")
+    all_indexes = (
+        indexes_can_append + indexes_cannot_append_with_other  # type: ignore[operator]
+    )
 
     @pytest.mark.parametrize("index", all_indexes, ids=lambda x: type(x).__name__)
     def test_append_same_columns_type(self, index):