From 60c2940fcf28ee84b64ebda813adfd78a68eea9f Mon Sep 17 00:00:00 2001
From: jbrockmendel <jbrockmendel@gmail.com>
Date: Sat, 26 Feb 2022 11:28:19 -0800
Subject: [PATCH] TYP: assorted (#46121)

---
 pandas/core/algorithms.py                    |  2 +-
 pandas/core/array_algos/masked_reductions.py | 37 ++++++++++----------
 pandas/core/array_algos/replace.py           |  7 ++--
 pandas/core/arrays/_mixins.py                |  2 +-
 pandas/core/arrays/masked.py                 |  6 ++--
 pandas/core/arrays/numeric.py                |  5 ++-
 pandas/core/groupby/groupby.py               |  4 +--
 pandas/core/groupby/ops.py                   |  8 ++---
 pandas/core/indexes/base.py                  |  4 ++-
 pandas/core/indexes/period.py                |  3 +-
 pandas/core/internals/array_manager.py       |  5 +--
 pandas/core/internals/blocks.py              | 17 ++++-----
 pandas/core/internals/managers.py            |  4 +--
 pandas/core/missing.py                       |  2 +-
 pandas/core/reshape/merge.py                 |  3 +-
 pandas/core/sorting.py                       | 31 ++++++++--------
 pandas/core/strings/object_array.py          |  2 +-
 pandas/core/tools/numeric.py                 |  3 +-
 pandas/core/util/hashing.py                  | 19 ++++++----
 pandas/io/pytables.py                        |  3 +-
 pandas/tests/frame/indexing/test_where.py    | 14 ++------
 pandas/tests/indexing/test_loc.py            |  6 ++--
 pandas/tests/plotting/test_datetimelike.py   |  4 +--
 23 files changed, 98 insertions(+), 93 deletions(-)

diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index c57261c810663..51817c82091fb 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -534,7 +534,7 @@ def factorize_array(
     na_sentinel: int = -1,
     size_hint: int | None = None,
     na_value=None,
-    mask: np.ndarray | None = None,
+    mask: npt.NDArray[np.bool_] | None = None,
 ) -> tuple[npt.NDArray[np.intp], np.ndarray]:
     """
     Factorize a numpy array to codes and uniques.
diff --git a/pandas/core/array_algos/masked_reductions.py b/pandas/core/array_algos/masked_reductions.py
index 66a3152de1499..3e59a267f7191 100644
--- a/pandas/core/array_algos/masked_reductions.py
+++ b/pandas/core/array_algos/masked_reductions.py
@@ -2,15 +2,14 @@
 masked_reductions.py is for reduction algorithms using a mask-based approach
 for missing values.
 """
+from __future__ import annotations
 
-from typing import (
-    Callable,
-    Optional,
-)
+from typing import Callable
 
 import numpy as np
 
 from pandas._libs import missing as libmissing
+from pandas._typing import npt
 
 from pandas.core.nanops import check_below_min_count
 
@@ -18,11 +17,11 @@
 def _sumprod(
     func: Callable,
     values: np.ndarray,
-    mask: np.ndarray,
+    mask: npt.NDArray[np.bool_],
     *,
     skipna: bool = True,
     min_count: int = 0,
-    axis: Optional[int] = None,
+    axis: int | None = None,
 ):
     """
     Sum or product for 1D masked array.
@@ -33,7 +32,7 @@ def _sumprod(
     values : np.ndarray
         Numpy array with the values (can be of any dtype that support the
         operation).
-    mask : np.ndarray
+    mask : np.ndarray[bool]
         Boolean numpy array (True values indicate missing values).
     skipna : bool, default True
         Whether to skip NA.
@@ -58,11 +57,11 @@ def _sumprod(
 
 def sum(
     values: np.ndarray,
-    mask: np.ndarray,
+    mask: npt.NDArray[np.bool_],
     *,
     skipna: bool = True,
     min_count: int = 0,
-    axis: Optional[int] = None,
+    axis: int | None = None,
 ):
     return _sumprod(
         np.sum, values=values, mask=mask, skipna=skipna, min_count=min_count, axis=axis
@@ -71,11 +70,11 @@ def sum(
 
 def prod(
     values: np.ndarray,
-    mask: np.ndarray,
+    mask: npt.NDArray[np.bool_],
     *,
     skipna: bool = True,
     min_count: int = 0,
-    axis: Optional[int] = None,
+    axis: int | None = None,
 ):
     return _sumprod(
         np.prod, values=values, mask=mask, skipna=skipna, min_count=min_count, axis=axis
@@ -85,10 +84,10 @@ def prod(
 def _minmax(
     func: Callable,
     values: np.ndarray,
-    mask: np.ndarray,
+    mask: npt.NDArray[np.bool_],
     *,
     skipna: bool = True,
-    axis: Optional[int] = None,
+    axis: int | None = None,
 ):
     """
     Reduction for 1D masked array.
@@ -99,7 +98,7 @@ def _minmax(
     values : np.ndarray
         Numpy array with the values (can be of any dtype that support the
         operation).
-    mask : np.ndarray
+    mask : np.ndarray[bool]
         Boolean numpy array (True values indicate missing values).
     skipna : bool, default True
         Whether to skip NA.
@@ -122,26 +121,26 @@ def _minmax(
 
 def min(
     values: np.ndarray,
-    mask: np.ndarray,
+    mask: npt.NDArray[np.bool_],
     *,
     skipna: bool = True,
-    axis: Optional[int] = None,
+    axis: int | None = None,
 ):
     return _minmax(np.min, values=values, mask=mask, skipna=skipna, axis=axis)
 
 
 def max(
     values: np.ndarray,
-    mask: np.ndarray,
+    mask: npt.NDArray[np.bool_],
     *,
     skipna: bool = True,
-    axis: Optional[int] = None,
+    axis: int | None = None,
 ):
     return _minmax(np.max, values=values, mask=mask, skipna=skipna, axis=axis)
 
 
 # TODO: axis kwarg
-def mean(values: np.ndarray, mask: np.ndarray, skipna: bool = True):
+def mean(values: np.ndarray, mask: npt.NDArray[np.bool_], skipna: bool = True):
     if not values.size or mask.all():
         return libmissing.NA
     _sum = _sumprod(np.sum, values=values, mask=mask, skipna=skipna)
diff --git a/pandas/core/array_algos/replace.py b/pandas/core/array_algos/replace.py
index e26bb9fb6ebad..19a44dbfe6f6d 100644
--- a/pandas/core/array_algos/replace.py
+++ b/pandas/core/array_algos/replace.py
@@ -15,6 +15,7 @@
 from pandas._typing import (
     ArrayLike,
     Scalar,
+    npt,
 )
 
 from pandas.core.dtypes.common import (
@@ -42,7 +43,7 @@ def should_use_regex(regex: bool, to_replace: Any) -> bool:
 
 
 def compare_or_regex_search(
-    a: ArrayLike, b: Scalar | Pattern, regex: bool, mask: np.ndarray
+    a: ArrayLike, b: Scalar | Pattern, regex: bool, mask: npt.NDArray[np.bool_]
 ) -> ArrayLike | bool:
     """
     Compare two array-like inputs of the same shape or two scalar values
@@ -116,7 +117,9 @@ def _check_comparison_types(
     return result
 
 
-def replace_regex(values: ArrayLike, rx: re.Pattern, value, mask: np.ndarray | None):
+def replace_regex(
+    values: ArrayLike, rx: re.Pattern, value, mask: npt.NDArray[np.bool_] | None
+):
     """
     Parameters
     ----------
diff --git a/pandas/core/arrays/_mixins.py b/pandas/core/arrays/_mixins.py
index 7c0997eae7311..cace08aac5c7e 100644
--- a/pandas/core/arrays/_mixins.py
+++ b/pandas/core/arrays/_mixins.py
@@ -367,7 +367,7 @@ def _putmask(self, mask: npt.NDArray[np.bool_], value) -> None:
         np.putmask(self._ndarray, mask, value)
 
     def _where(
-        self: NDArrayBackedExtensionArrayT, mask: np.ndarray, value
+        self: NDArrayBackedExtensionArrayT, mask: npt.NDArray[np.bool_], value
     ) -> NDArrayBackedExtensionArrayT:
         """
         Analogue to np.where(mask, self, value)
diff --git a/pandas/core/arrays/masked.py b/pandas/core/arrays/masked.py
index 76bbbfe2ebcb3..248eef0bb4430 100644
--- a/pandas/core/arrays/masked.py
+++ b/pandas/core/arrays/masked.py
@@ -99,13 +99,15 @@ class BaseMaskedArray(OpsMixin, ExtensionArray):
     _internal_fill_value: Scalar
     # our underlying data and mask are each ndarrays
     _data: np.ndarray
-    _mask: 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
 
-    def __init__(self, values: np.ndarray, mask: np.ndarray, copy: bool = False):
+    def __init__(
+        self, values: np.ndarray, mask: npt.NDArray[np.bool_], copy: bool = False
+    ):
         # values is supposed to already be validated in the subclass
         if not (isinstance(mask, np.ndarray) and mask.dtype == np.bool_):
             raise TypeError(
diff --git a/pandas/core/arrays/numeric.py b/pandas/core/arrays/numeric.py
index 4e5f4563f791e..3d957a2a1f34b 100644
--- a/pandas/core/arrays/numeric.py
+++ b/pandas/core/arrays/numeric.py
@@ -17,6 +17,7 @@
 from pandas._typing import (
     Dtype,
     DtypeObj,
+    npt,
 )
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import cache_readonly
@@ -219,7 +220,9 @@ class NumericArray(BaseMaskedArray):
 
     _dtype_cls: type[NumericDtype]
 
-    def __init__(self, values: np.ndarray, mask: np.ndarray, copy: bool = False):
+    def __init__(
+        self, values: np.ndarray, mask: npt.NDArray[np.bool_], copy: bool = False
+    ):
         checker = self._dtype_cls._checker
         if not (isinstance(values, np.ndarray) and checker(values.dtype)):
             descr = (
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 309b6a86be095..e47004bba2f50 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -3607,13 +3607,13 @@ def tail(self, n=5):
         return self._mask_selected_obj(mask)
 
     @final
-    def _mask_selected_obj(self, mask: np.ndarray) -> NDFrameT:
+    def _mask_selected_obj(self, mask: npt.NDArray[np.bool_]) -> NDFrameT:
         """
         Return _selected_obj with mask applied to the correct axis.
 
         Parameters
         ----------
-        mask : np.ndarray
+        mask : np.ndarray[bool]
             Boolean mask to apply.
 
         Returns
diff --git a/pandas/core/groupby/ops.py b/pandas/core/groupby/ops.py
index 252ebc075de57..cd153577bf4e0 100644
--- a/pandas/core/groupby/ops.py
+++ b/pandas/core/groupby/ops.py
@@ -437,8 +437,8 @@ def _cython_op_ndim_compat(
         min_count: int,
         ngroups: int,
         comp_ids: np.ndarray,
-        mask: np.ndarray | None = None,
-        result_mask: np.ndarray | None = None,
+        mask: npt.NDArray[np.bool_] | None = None,
+        result_mask: npt.NDArray[np.bool_] | None = None,
         **kwargs,
     ) -> np.ndarray:
         if values.ndim == 1:
@@ -481,8 +481,8 @@ def _call_cython_op(
         min_count: int,
         ngroups: int,
         comp_ids: np.ndarray,
-        mask: np.ndarray | None,
-        result_mask: np.ndarray | None,
+        mask: npt.NDArray[np.bool_] | None,
+        result_mask: npt.NDArray[np.bool_] | None,
         **kwargs,
     ) -> np.ndarray:  # np.ndarray[ndim=2]
         orig_values = values
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index d7594f2483569..108646f8766a6 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -5548,7 +5548,9 @@ def asof(self, label):
 
         return self[loc]
 
-    def asof_locs(self, where: Index, mask: np.ndarray) -> npt.NDArray[np.intp]:
+    def asof_locs(
+        self, where: Index, mask: npt.NDArray[np.bool_]
+    ) -> npt.NDArray[np.intp]:
         """
         Return the locations (indices) of labels in the index.
 
diff --git a/pandas/core/indexes/period.py b/pandas/core/indexes/period.py
index aba834a47ffef..006f53ba06c71 100644
--- a/pandas/core/indexes/period.py
+++ b/pandas/core/indexes/period.py
@@ -23,6 +23,7 @@
 from pandas._typing import (
     Dtype,
     DtypeObj,
+    npt,
 )
 from pandas.util._decorators import doc
 from pandas.util._exceptions import find_stack_level
@@ -327,7 +328,7 @@ def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
     # ------------------------------------------------------------------------
     # Index Methods
 
-    def asof_locs(self, where: Index, mask: np.ndarray) -> np.ndarray:
+    def asof_locs(self, where: Index, mask: npt.NDArray[np.bool_]) -> np.ndarray:
         """
         where : array of timestamps
         mask : np.ndarray[bool]
diff --git a/pandas/core/internals/array_manager.py b/pandas/core/internals/array_manager.py
index c7ec4f35e0ff1..3e499c99ac144 100644
--- a/pandas/core/internals/array_manager.py
+++ b/pandas/core/internals/array_manager.py
@@ -21,6 +21,7 @@
 from pandas._typing import (
     ArrayLike,
     DtypeObj,
+    npt,
 )
 from pandas.util._validators import validate_bool_kwarg
 
@@ -568,7 +569,7 @@ def reindex_indexer(
     def _reindex_indexer(
         self: T,
         new_axis,
-        indexer,
+        indexer: npt.NDArray[np.intp] | None,
         axis: int,
         fill_value=None,
         allow_dups: bool = False,
@@ -579,7 +580,7 @@ def _reindex_indexer(
         Parameters
         ----------
         new_axis : Index
-        indexer : ndarray of int64 or None
+        indexer : ndarray[intp] or None
         axis : int
         fill_value : object, default None
         allow_dups : bool, default False
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 5acba7960111e..42c6f03a3af93 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -87,7 +87,6 @@
     replace_regex,
     should_use_regex,
 )
-from pandas.core.array_algos.take import take_nd
 from pandas.core.array_algos.transforms import shift
 from pandas.core.arrays import (
     Categorical,
@@ -736,7 +735,7 @@ def _replace_coerce(
         self,
         to_replace,
         value,
-        mask: np.ndarray,
+        mask: npt.NDArray[np.bool_],
         inplace: bool = True,
         regex: bool = False,
     ) -> list[Block]:
@@ -827,19 +826,14 @@ def set_inplace(self, locs, values: ArrayLike) -> None:
 
     def take_nd(
         self,
-        indexer,
+        indexer: npt.NDArray[np.intp],
         axis: int,
         new_mgr_locs: BlockPlacement | None = None,
         fill_value=lib.no_default,
     ) -> Block:
         """
-        Take values according to indexer and return them as a block.bb
-
+        Take values according to indexer and return them as a block.
         """
-        # algos.take_nd dispatches for DatetimeTZBlock, CategoricalBlock
-        # so need to preserve types
-        # sparse is treated like an ndarray, but needs .get_values() shaping
-
         values = self.values
 
         if fill_value is lib.no_default:
@@ -848,6 +842,7 @@ def take_nd(
         else:
             allow_fill = True
 
+        # Note: algos.take_nd has upcast logic similar to coerce_to_target_dtype
         new_values = algos.take_nd(
             values, indexer, axis=axis, allow_fill=allow_fill, fill_value=fill_value
         )
@@ -1727,7 +1722,7 @@ def is_numeric(self):
 
     def take_nd(
         self,
-        indexer,
+        indexer: npt.NDArray[np.intp],
         axis: int = 0,
         new_mgr_locs: BlockPlacement | None = None,
         fill_value=lib.no_default,
@@ -2259,7 +2254,7 @@ def to_native_types(
     """convert to our native types format"""
     if isinstance(values, Categorical):
         # GH#40754 Convert categorical datetimes to datetime array
-        values = take_nd(
+        values = algos.take_nd(
             values.categories._values,
             ensure_platform_int(values._codes),
             fill_value=na_rep,
diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py
index 6297a7578ccd4..4b8f1aae75b1b 100644
--- a/pandas/core/internals/managers.py
+++ b/pandas/core/internals/managers.py
@@ -597,7 +597,7 @@ def consolidate(self: T) -> T:
     def reindex_indexer(
         self: T,
         new_axis: Index,
-        indexer,
+        indexer: npt.NDArray[np.intp] | None,
         axis: int,
         fill_value=None,
         allow_dups: bool = False,
@@ -610,7 +610,7 @@ def reindex_indexer(
         Parameters
         ----------
         new_axis : Index
-        indexer : ndarray of int64 or None
+        indexer : ndarray[intp] or None
         axis : int
         fill_value : object, default None
         allow_dups : bool, default False
diff --git a/pandas/core/missing.py b/pandas/core/missing.py
index d922cfd06f7ac..46ea23e431d15 100644
--- a/pandas/core/missing.py
+++ b/pandas/core/missing.py
@@ -43,7 +43,7 @@
     from pandas import Index
 
 
-def check_value_size(value, mask: np.ndarray, length: int):
+def check_value_size(value, mask: npt.NDArray[np.bool_], length: int):
     """
     Validate the size of the values passed to ExtensionArray.fillna.
     """
diff --git a/pandas/core/reshape/merge.py b/pandas/core/reshape/merge.py
index 44ead165fec25..46f74c5fc67e7 100644
--- a/pandas/core/reshape/merge.py
+++ b/pandas/core/reshape/merge.py
@@ -2276,14 +2276,13 @@ def _factorize_keys(
 
 
 def _sort_labels(
-    uniques: np.ndarray, left: np.ndarray, right: np.ndarray
+    uniques: np.ndarray, left: npt.NDArray[np.intp], right: npt.NDArray[np.intp]
 ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
 
     llength = len(left)
     labels = np.concatenate([left, right])
 
     _, new_labels = algos.safe_sort(uniques, labels, na_sentinel=-1)
-    assert new_labels.dtype == np.intp
     new_left, new_right = new_labels[:llength], new_labels[llength:]
 
     return new_left, new_right
diff --git a/pandas/core/sorting.py b/pandas/core/sorting.py
index 7ab53ccf7cb8d..21d9107a61cb7 100644
--- a/pandas/core/sorting.py
+++ b/pandas/core/sorting.py
@@ -41,6 +41,7 @@
 
 if TYPE_CHECKING:
     from pandas import MultiIndex
+    from pandas.core.arrays import ExtensionArray
     from pandas.core.indexes.base import Index
 
 
@@ -52,7 +53,7 @@ def get_indexer_indexer(
     na_position: str,
     sort_remaining: bool,
     key: IndexKeyFunc,
-) -> np.ndarray | None:
+) -> npt.NDArray[np.intp] | None:
     """
     Helper method that return the indexer according to input parameters for
     the sort_index method of DataFrame and Series.
@@ -69,7 +70,7 @@ def get_indexer_indexer(
 
     Returns
     -------
-    Optional[ndarray]
+    Optional[ndarray[intp]]
         The indexer for the new index.
     """
 
@@ -215,7 +216,7 @@ def get_compressed_ids(
     return compress_group_index(ids, sort=True)
 
 
-def is_int64_overflow_possible(shape) -> bool:
+def is_int64_overflow_possible(shape: Shape) -> bool:
     the_prod = 1
     for x in shape:
         the_prod *= int(x)
@@ -223,7 +224,7 @@ def is_int64_overflow_possible(shape) -> bool:
     return the_prod >= lib.i8max
 
 
-def decons_group_index(comp_labels, shape):
+def decons_group_index(comp_labels, shape: Shape):
     # reconstruct labels
     if is_int64_overflow_possible(shape):
         # at some point group indices are factorized,
@@ -244,7 +245,7 @@ def decons_group_index(comp_labels, shape):
 
 
 def decons_obs_group_ids(
-    comp_ids: npt.NDArray[np.intp], obs_ids, shape, labels, xnull: bool
+    comp_ids: npt.NDArray[np.intp], obs_ids, shape: Shape, labels, xnull: bool
 ):
     """
     Reconstruct labels from observed group ids.
@@ -428,7 +429,7 @@ def nargsort(
     return ensure_platform_int(indexer)
 
 
-def nargminmax(values, method: str, axis: int = 0):
+def nargminmax(values: ExtensionArray, method: str, axis: int = 0):
     """
     Implementation of np.argmin/argmax but for ExtensionArray and which
     handles missing values.
@@ -447,21 +448,21 @@ def nargminmax(values, method: str, axis: int = 0):
     func = np.argmax if method == "argmax" else np.argmin
 
     mask = np.asarray(isna(values))
-    values = values._values_for_argsort()
+    arr_values = values._values_for_argsort()
 
-    if values.ndim > 1:
+    if arr_values.ndim > 1:
         if mask.any():
             if axis == 1:
-                zipped = zip(values, mask)
+                zipped = zip(arr_values, mask)
             else:
-                zipped = zip(values.T, mask.T)
+                zipped = zip(arr_values.T, mask.T)
             return np.array([_nanargminmax(v, m, func) for v, m in zipped])
-        return func(values, axis=axis)
+        return func(arr_values, axis=axis)
 
-    return _nanargminmax(values, mask, func)
+    return _nanargminmax(arr_values, mask, func)
 
 
-def _nanargminmax(values, mask, func) -> int:
+def _nanargminmax(values: np.ndarray, mask: npt.NDArray[np.bool_], func) -> int:
     """
     See nanargminmax.__doc__.
     """
@@ -591,9 +592,9 @@ def get_indexer_dict(
     dict:
         Labels mapped to indexers.
     """
-    shape = [len(x) for x in keys]
+    shape = tuple(len(x) for x in keys)
 
-    group_index = get_group_index(label_list, tuple(shape), sort=True, xnull=True)
+    group_index = get_group_index(label_list, shape, sort=True, xnull=True)
     if np.all(group_index == -1):
         # Short-circuit, lib.indices_fast will return the same
         return {}
diff --git a/pandas/core/strings/object_array.py b/pandas/core/strings/object_array.py
index 6b0380a292f07..2f65ce17f93b2 100644
--- a/pandas/core/strings/object_array.py
+++ b/pandas/core/strings/object_array.py
@@ -62,7 +62,7 @@ def _str_map(
             na_value = self._str_na_value
 
         if not len(self):
-            return np.ndarray(0, dtype=dtype)
+            return np.array([], dtype=dtype)
 
         arr = np.asarray(self, dtype=object)
         mask = isna(arr)
diff --git a/pandas/core/tools/numeric.py b/pandas/core/tools/numeric.py
index 26197e1ac4847..ef7f4bc92e25b 100644
--- a/pandas/core/tools/numeric.py
+++ b/pandas/core/tools/numeric.py
@@ -3,6 +3,7 @@
 import numpy as np
 
 from pandas._libs import lib
+from pandas._typing import npt
 
 from pandas.core.dtypes.cast import maybe_downcast_numeric
 from pandas.core.dtypes.common import (
@@ -167,7 +168,7 @@ def to_numeric(arg, errors="raise", downcast=None):
 
     # GH33013: for IntegerArray & FloatingArray extract non-null values for casting
     # save mask to reconstruct the full array after casting
-    mask: np.ndarray | None = None
+    mask: npt.NDArray[np.bool_] | None = None
     if isinstance(values, NumericArray):
         mask = values._mask
         values = values._data[~mask]
diff --git a/pandas/core/util/hashing.py b/pandas/core/util/hashing.py
index 79db60ab5a7ce..5a5e46e0227aa 100644
--- a/pandas/core/util/hashing.py
+++ b/pandas/core/util/hashing.py
@@ -16,7 +16,10 @@
 
 from pandas._libs import lib
 from pandas._libs.hashing import hash_object_array
-from pandas._typing import ArrayLike
+from pandas._typing import (
+    ArrayLike,
+    npt,
+)
 
 from pandas.core.dtypes.common import (
     is_categorical_dtype,
@@ -44,7 +47,9 @@
 _default_hash_key = "0123456789123456"
 
 
-def combine_hash_arrays(arrays: Iterator[np.ndarray], num_items: int) -> np.ndarray:
+def combine_hash_arrays(
+    arrays: Iterator[np.ndarray], num_items: int
+) -> npt.NDArray[np.uint64]:
     """
     Parameters
     ----------
@@ -172,7 +177,7 @@ def hash_tuples(
     vals: MultiIndex | Iterable[tuple[Hashable, ...]],
     encoding: str = "utf8",
     hash_key: str = _default_hash_key,
-) -> np.ndarray:
+) -> npt.NDArray[np.uint64]:
     """
     Hash an MultiIndex / listlike-of-tuples efficiently.
 
@@ -214,7 +219,9 @@ def hash_tuples(
     return h
 
 
-def _hash_categorical(cat: Categorical, encoding: str, hash_key: str) -> np.ndarray:
+def _hash_categorical(
+    cat: Categorical, encoding: str, hash_key: str
+) -> npt.NDArray[np.uint64]:
     """
     Hash a Categorical by hashing its categories, and then mapping the codes
     to the hashes
@@ -257,7 +264,7 @@ def hash_array(
     encoding: str = "utf8",
     hash_key: str = _default_hash_key,
     categorize: bool = True,
-) -> np.ndarray:
+) -> npt.NDArray[np.uint64]:
     """
     Given a 1d array, return an array of deterministic integers.
 
@@ -306,7 +313,7 @@ def _hash_ndarray(
     encoding: str = "utf8",
     hash_key: str = _default_hash_key,
     categorize: bool = True,
-) -> np.ndarray:
+) -> npt.NDArray[np.uint64]:
     """
     See hash_array.__doc__.
     """
diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py
index 53b5079ed6be9..7c784c1489617 100644
--- a/pandas/io/pytables.py
+++ b/pandas/io/pytables.py
@@ -41,6 +41,7 @@
     ArrayLike,
     DtypeArg,
     Shape,
+    npt,
 )
 from pandas.compat._optional import import_optional_dependency
 from pandas.compat.pickle_compat import patch_pickle
@@ -4409,7 +4410,7 @@ def write_data_chunk(
         self,
         rows: np.ndarray,
         indexes: list[np.ndarray],
-        mask: np.ndarray | None,
+        mask: npt.NDArray[np.bool_] | None,
         values: list[np.ndarray],
     ):
         """
diff --git a/pandas/tests/frame/indexing/test_where.py b/pandas/tests/frame/indexing/test_where.py
index 51013adc0b907..62d1f89814206 100644
--- a/pandas/tests/frame/indexing/test_where.py
+++ b/pandas/tests/frame/indexing/test_where.py
@@ -770,25 +770,15 @@ def test_where_try_cast_deprecated(frame_or_series):
         obj.where(mask, -1, try_cast=False)
 
 
-def test_where_int_downcasting_deprecated(using_array_manager, request):
+def test_where_int_downcasting_deprecated():
     # GH#44597
-    if not using_array_manager:
-        mark = pytest.mark.xfail(
-            reason="After fixing a bug in can_hold_element, we don't go through "
-            "the deprecated path, and also up-cast both columns to int32 "
-            "instead of just 1."
-        )
-        request.node.add_marker(mark)
     arr = np.arange(6).astype(np.int16).reshape(3, 2)
     df = DataFrame(arr)
 
     mask = np.zeros(arr.shape, dtype=bool)
     mask[:, 0] = True
 
-    msg = "Downcasting integer-dtype"
-    warn = FutureWarning if not using_array_manager else None
-    with tm.assert_produces_warning(warn, match=msg):
-        res = df.where(mask, 2**17)
+    res = df.where(mask, 2**17)
 
     expected = DataFrame({0: arr[:, 0], 1: np.array([2**17] * 3, dtype=np.int32)})
     tm.assert_frame_equal(res, expected)
diff --git a/pandas/tests/indexing/test_loc.py b/pandas/tests/indexing/test_loc.py
index d16d086a37d3d..4702b5e5c4504 100644
--- a/pandas/tests/indexing/test_loc.py
+++ b/pandas/tests/indexing/test_loc.py
@@ -44,16 +44,16 @@
 
 
 @pytest.mark.parametrize(
-    "series, new_serie, expected_ser",
+    "series, new_series, expected_ser",
     [
         [[np.nan, np.nan, "b"], ["a", np.nan, np.nan], [False, True, True]],
         [[np.nan, "b"], ["a", np.nan], [False, True]],
     ],
 )
-def test_not_change_nan_loc(series, new_serie, expected_ser):
+def test_not_change_nan_loc(series, new_series, expected_ser):
     # GH 28403
     df = DataFrame({"A": series})
-    df["A"].loc[:] = new_serie
+    df["A"].loc[:] = new_series
     expected = DataFrame({"A": expected_ser})
     tm.assert_frame_equal(df.isna(), expected)
     tm.assert_frame_equal(df.notna(), ~expected)
diff --git a/pandas/tests/plotting/test_datetimelike.py b/pandas/tests/plotting/test_datetimelike.py
index 1b818f57d0572..197083a9ad940 100644
--- a/pandas/tests/plotting/test_datetimelike.py
+++ b/pandas/tests/plotting/test_datetimelike.py
@@ -523,7 +523,7 @@ def test_finder_hourly(self):
 
     def test_gaps(self):
         ts = tm.makeTimeSeries()
-        ts[5:25] = np.nan
+        ts.iloc[5:25] = np.nan
         _, ax = self.plt.subplots()
         ts.plot(ax=ax)
         lines = ax.get_lines()
@@ -541,7 +541,7 @@ def test_gaps(self):
         # irregular
         ts = tm.makeTimeSeries()
         ts = ts[[0, 1, 2, 5, 7, 9, 12, 15, 20]]
-        ts[2:5] = np.nan
+        ts.iloc[2:5] = np.nan
         _, ax = self.plt.subplots()
         ax = ts.plot(ax=ax)
         lines = ax.get_lines()