diff --git a/pandas/_libs/hashtable_class_helper.pxi.in b/pandas/_libs/hashtable_class_helper.pxi.in
index eae393f33bfd3..e66d56391f3b8 100644
--- a/pandas/_libs/hashtable_class_helper.pxi.in
+++ b/pandas/_libs/hashtable_class_helper.pxi.in
@@ -1070,7 +1070,7 @@ cdef class StringHashTable(HashTable):
             val = values[i]
 
             if isinstance(val, str):
-                # GH#31499 if we have a np.str_ PyUnicode_AsUTF8 won't recognize
+                # GH#31499 if we have an np.str_ PyUnicode_AsUTF8 won't recognize
                 #  it as a str, even though isinstance does.
                 v = PyUnicode_AsUTF8(<str>val)
             else:
@@ -1108,7 +1108,7 @@ cdef class StringHashTable(HashTable):
             val = values[i]
 
             if isinstance(val, str):
-                # GH#31499 if we have a np.str_ PyUnicode_AsUTF8 won't recognize
+                # GH#31499 if we have an np.str_ PyUnicode_AsUTF8 won't recognize
                 #  it as a str, even though isinstance does.
                 v = PyUnicode_AsUTF8(<str>val)
             else:
diff --git a/pandas/_libs/index.pyx b/pandas/_libs/index.pyx
index f9e382285b408..3a3f1b6305f65 100644
--- a/pandas/_libs/index.pyx
+++ b/pandas/_libs/index.pyx
@@ -58,7 +58,7 @@ cdef bint is_definitely_invalid_key(object val):
 
 cdef ndarray _get_bool_indexer(ndarray values, object val, ndarray mask = None):
     """
-    Return a ndarray[bool] of locations where val matches self.values.
+    Return an ndarray[bool] of locations where val matches self.values.
 
     If val is not NA, this is equivalent to `self.values == val`
     """
diff --git a/pandas/_libs/tslib.pyx b/pandas/_libs/tslib.pyx
index 3c5854602df53..2a53e604423d8 100644
--- a/pandas/_libs/tslib.pyx
+++ b/pandas/_libs/tslib.pyx
@@ -120,7 +120,7 @@ def format_array_from_datetime(
     NPY_DATETIMEUNIT reso=NPY_FR_ns,
 ) -> np.ndarray:
     """
-    return a np object array of the string formatted values
+    return an np object array of the string formatted values
 
     Parameters
     ----------
diff --git a/pandas/_libs/tslibs/conversion.pyx b/pandas/_libs/tslibs/conversion.pyx
index e8ec640fa539f..a73a73d327f7a 100644
--- a/pandas/_libs/tslibs/conversion.pyx
+++ b/pandas/_libs/tslibs/conversion.pyx
@@ -276,7 +276,7 @@ cdef (int64_t, int) precision_from_unit(
 
 cdef int64_t get_datetime64_nanos(object val, NPY_DATETIMEUNIT reso) except? -1:
     """
-    Extract the value and unit from a np.datetime64 object, then convert the
+    Extract the value and unit from an np.datetime64 object, then convert the
     value to nanoseconds if necessary.
     """
     cdef:
diff --git a/pandas/_libs/tslibs/nattype.pyx b/pandas/_libs/tslibs/nattype.pyx
index 49c1c46ea4935..195e649be48d3 100644
--- a/pandas/_libs/tslibs/nattype.pyx
+++ b/pandas/_libs/tslibs/nattype.pyx
@@ -1885,7 +1885,7 @@ cdef bint checknull_with_nat(object val):
 
 cdef bint is_dt64nat(object val):
     """
-    Is this a np.datetime64 object np.datetime64("NaT").
+    Is this an np.datetime64 object np.datetime64("NaT").
     """
     if cnp.is_datetime64_object(val):
         return cnp.get_datetime64_value(val) == NPY_NAT
@@ -1894,7 +1894,7 @@ cdef bint is_dt64nat(object val):
 
 cdef bint is_td64nat(object val):
     """
-    Is this a np.timedelta64 object np.timedelta64("NaT").
+    Is this an np.timedelta64 object np.timedelta64("NaT").
     """
     if cnp.is_timedelta64_object(val):
         return cnp.get_timedelta64_value(val) == NPY_NAT
diff --git a/pandas/_libs/tslibs/offsets.pyx b/pandas/_libs/tslibs/offsets.pyx
index 22bc97f959520..f2bf18fba8c42 100644
--- a/pandas/_libs/tslibs/offsets.pyx
+++ b/pandas/_libs/tslibs/offsets.pyx
@@ -818,7 +818,7 @@ cdef class BaseOffset:
         state["normalize"] = self.normalize
 
         # we don't want to actually pickle the calendar object
-        # as its a np.busyday; we recreate on deserialization
+        # as its an np.busyday; we recreate on deserialization
         state.pop("calendar", None)
         if "kwds" in state:
             state["kwds"].pop("calendar", None)
diff --git a/pandas/_libs/tslibs/timedeltas.pyx b/pandas/_libs/tslibs/timedeltas.pyx
index 4d30415a396db..8d23e4099abd0 100644
--- a/pandas/_libs/tslibs/timedeltas.pyx
+++ b/pandas/_libs/tslibs/timedeltas.pyx
@@ -1489,7 +1489,7 @@ cdef class _Timedelta(timedelta):
         """
         cdef:
             str abbrev = npy_unit_to_abbrev(self._creso)
-        # TODO: way to create a np.timedelta64 obj with the reso directly
+        # TODO: way to create an np.timedelta64 obj with the reso directly
         #  instead of having to get the abbrev?
         return np.timedelta64(self._value, abbrev)
 
diff --git a/pandas/_libs/tslibs/timestamps.pyx b/pandas/_libs/tslibs/timestamps.pyx
index b2a2918f95b4c..a73fe266faac5 100644
--- a/pandas/_libs/tslibs/timestamps.pyx
+++ b/pandas/_libs/tslibs/timestamps.pyx
@@ -412,7 +412,7 @@ cdef class _Timestamp(ABCTimestamp):
 
     @classmethod
     def _from_dt64(cls, dt64: np.datetime64):
-        # construct a Timestamp from a np.datetime64 object, keeping the
+        # construct a Timestamp from an np.datetime64 object, keeping the
         #  resolution of the input.
         # This is here mainly so we can incrementally implement non-nano
         #  (e.g. only tznaive at first)
diff --git a/pandas/_testing/__init__.py b/pandas/_testing/__init__.py
index f19aa0bb487e4..230ebc3c10fad 100644
--- a/pandas/_testing/__init__.py
+++ b/pandas/_testing/__init__.py
@@ -402,7 +402,7 @@ def get_cython_table_params(ndframe, func_names_and_expected):
     ----------
     ndframe : DataFrame or Series
     func_names_and_expected : Sequence of two items
-        The first item is a name of a NDFrame method ('sum', 'prod') etc.
+        The first item is a name of an NDFrame method ('sum', 'prod') etc.
         The second item is the expected return value.
 
     Returns
diff --git a/pandas/_testing/asserters.py b/pandas/_testing/asserters.py
index 160c362d0dbc1..3732fd9d20655 100644
--- a/pandas/_testing/asserters.py
+++ b/pandas/_testing/asserters.py
@@ -803,7 +803,7 @@ def assert_extension_array_equal(
             ):
                 return
         # Avoid slow object-dtype comparisons
-        # np.asarray for case where we have a np.MaskedArray
+        # np.asarray for case where we have an np.MaskedArray
         assert_numpy_array_equal(
             np.asarray(left.asi8),
             np.asarray(right.asi8),
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index 95e03eae2133c..a12ae07c2522f 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -1161,7 +1161,7 @@ def _sub_datetimelike_scalar(
             raise TypeError(f"cannot subtract a datelike from a {type(self).__name__}")
 
         self = cast("DatetimeArray", self)
-        # subtract a datetime from myself, yielding a ndarray[timedelta64[ns]]
+        # subtract a datetime from myself, yielding an ndarray[timedelta64[ns]]
 
         if isna(other):
             # i.e. np.datetime64("NaT")
diff --git a/pandas/core/arrays/interval.py b/pandas/core/arrays/interval.py
index b731af1e94597..a7966dc0dee82 100644
--- a/pandas/core/arrays/interval.py
+++ b/pandas/core/arrays/interval.py
@@ -1977,7 +1977,7 @@ def repeat(
         axis: AxisInt | None = None,
     ) -> Self:
         """
-        Repeat elements of a IntervalArray.
+        Repeat elements of an IntervalArray.
 
         Returns a new IntervalArray where each element of the current IntervalArray
         is repeated consecutively a given number of times.
diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index d91edcf418c45..2dc45bd752ea5 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -393,7 +393,7 @@ def trans(x):
 
 def maybe_upcast_numeric_to_64bit(arr: NumpyIndexT) -> NumpyIndexT:
     """
-    If array is a int/uint/float bit size lower than 64 bit, upcast it to 64 bit.
+    If array is an int/uint/float bit size lower than 64 bit, upcast it to 64 bit.
 
     Parameters
     ----------
@@ -1393,7 +1393,7 @@ def construct_1d_arraylike_from_scalar(
     value: Scalar, length: int, dtype: DtypeObj | None
 ) -> ArrayLike:
     """
-    create a np.ndarray / pandas type of specified shape and dtype
+    create an np.ndarray / pandas type of specified shape and dtype
     filled with values
 
     Parameters
diff --git a/pandas/core/dtypes/common.py b/pandas/core/dtypes/common.py
index bb3e8105d5472..5ecdcc9c2389d 100644
--- a/pandas/core/dtypes/common.py
+++ b/pandas/core/dtypes/common.py
@@ -602,7 +602,7 @@ def is_categorical_dtype(arr_or_dtype) -> bool:
 
 def is_string_or_object_np_dtype(dtype: np.dtype) -> bool:
     """
-    Faster alternative to is_string_dtype, assumes we have a np.dtype object.
+    Faster alternative to is_string_dtype, assumes we have an np.dtype object.
     """
     return dtype == object or dtype.kind in "SU"
 
diff --git a/pandas/core/dtypes/dtypes.py b/pandas/core/dtypes/dtypes.py
index 90cc88f165445..3b14c5348f9d6 100644
--- a/pandas/core/dtypes/dtypes.py
+++ b/pandas/core/dtypes/dtypes.py
@@ -113,7 +113,7 @@
 
 class PandasExtensionDtype(ExtensionDtype):
     """
-    A np.dtype duck-typed class, suitable for holding a custom dtype.
+    An np.dtype duck-typed class, suitable for holding a custom dtype.
 
     THIS IS NOT A REAL NUMPY DTYPE
     """
@@ -503,7 +503,7 @@ def _hash_categories(self) -> int:
             # assumes if any individual category is a tuple, then all our. ATM
             # I don't really want to support just some of the categories being
             # tuples.
-            cat_list = list(categories)  # breaks if a np.array of categories
+            cat_list = list(categories)  # breaks if an np.array of categories
             cat_array = hash_tuples(cat_list)
         else:
             if categories.dtype == "O" and len({type(x) for x in categories}) != 1:
diff --git a/pandas/core/dtypes/missing.py b/pandas/core/dtypes/missing.py
index 73d0a0924aa27..5fd40aebe3108 100644
--- a/pandas/core/dtypes/missing.py
+++ b/pandas/core/dtypes/missing.py
@@ -593,7 +593,7 @@ def construct_1d_array_from_inferred_fill_value(
 
 def maybe_fill(arr: np.ndarray) -> np.ndarray:
     """
-    Fill numpy.ndarray with NaN, unless we have a integer or boolean dtype.
+    Fill numpy.ndarray with NaN, unless we have an integer or boolean dtype.
     """
     if arr.dtype.kind not in "iub":
         arr.fill(np.nan)
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index f7868b9e46c37..2c8ec599a19ef 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -645,7 +645,7 @@ def _get_indices(self, names):
 
         def get_converter(s):
             # possibly convert to the actual key types
-            # in the indices, could be a Timestamp or a np.datetime64
+            # in the indices, could be a Timestamp or an np.datetime64
             if isinstance(s, datetime.datetime):
                 return lambda key: Timestamp(key)
             elif isinstance(s, np.datetime64):
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index ad1233b02f9f2..17355b41563cb 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -1319,7 +1319,7 @@ def _maybe_disallow_fill(self, allow_fill: bool, fill_value, indices) -> bool:
 
     def repeat(self, repeats, axis: None = None) -> Self:
         """
-        Repeat elements of a Index.
+        Repeat elements of an Index.
 
         Returns a new Index where each element of the current Index
         is repeated consecutively a given number of times.
@@ -4937,7 +4937,7 @@ def _can_use_libjoin(self) -> bool:
         Whether we can use the fastpaths implemented in _libs.join.
 
         This is driven by whether (in monotonic increasing cases that are
-        guaranteed not to have NAs) we can convert to a np.ndarray without
+        guaranteed not to have NAs) we can convert to an np.ndarray without
         making a copy. If we cannot, this negates the performance benefit
         of using libjoin.
         """
diff --git a/pandas/core/indexes/range.py b/pandas/core/indexes/range.py
index c5dcb53d3c81c..bf05dbd40145f 100644
--- a/pandas/core/indexes/range.py
+++ b/pandas/core/indexes/range.py
@@ -921,7 +921,7 @@ def _union(self, other: Index, sort: bool | None):
         sort : bool or None, default None
             Whether to sort (monotonically increasing) the resulting index.
             ``sort=None|True`` returns a ``RangeIndex`` if possible or a sorted
-            ``Index`` with a int64 dtype if not.
+            ``Index`` with an int64 dtype if not.
             ``sort=False`` can return a ``RangeIndex`` if self is monotonically
             increasing and other is fully contained in self. Otherwise, returns
             an unsorted ``Index`` with an int64 dtype.
@@ -1184,7 +1184,7 @@ def _concat(self, indexes: list[Index], name: Hashable) -> Index:
         Overriding parent method for the case of all RangeIndex instances.
 
         When all members of "indexes" are of type RangeIndex: result will be
-        RangeIndex if possible, Index with a int64 dtype otherwise. E.g.:
+        RangeIndex if possible, Index with an int64 dtype otherwise. E.g.:
         indexes = [RangeIndex(3), RangeIndex(3, 6)] -> RangeIndex(6)
         indexes = [RangeIndex(3), RangeIndex(4, 6)] -> Index([0,1,2,4,5], dtype='int64')
         """
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index 5cda56b9956ea..ece469e163d04 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -1480,7 +1480,7 @@ def _get_slice_axis(self, slice_obj: slice, axis: AxisInt):
     def _convert_to_indexer(self, key, axis: AxisInt):
         """
         Convert indexing key into something we can use to do actual fancy
-        indexing on a ndarray.
+        indexing on an ndarray.
 
         Examples
         ix[:5] -> slice(0, 5)
@@ -1991,7 +1991,7 @@ def _setitem_with_indexer_split_path(self, indexer, value, name: str):
         lplane_indexer = length_of_indexer(pi, self.obj.index)
         # lplane_indexer gives the expected length of obj[indexer[0]]
 
-        # we need an iterable, with a ndim of at least 1
+        # we need an iterable, with an ndim of at least 1
         # eg. don't pass through np.array(0)
         if is_list_like_indexer(value) and getattr(value, "ndim", 1) > 0:
             if isinstance(value, ABCDataFrame):
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 32d6c8f738851..f0d4a76d8d34e 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -1151,7 +1151,7 @@ def putmask(self, mask, new) -> list[Block]:
         Parameters
         ----------
         mask : np.ndarray[bool], SparseArray[bool], or BooleanArray
-        new : a ndarray/object
+        new : an ndarray/object
 
         Returns
         -------
@@ -1214,7 +1214,7 @@ def where(self, other, cond) -> list[Block]:
 
         Parameters
         ----------
-        other : a ndarray/object
+        other : an ndarray/object
         cond : np.ndarray[bool], SparseArray[bool], or BooleanArray
 
         Returns
diff --git a/pandas/core/internals/construction.py b/pandas/core/internals/construction.py
index 135ddf58076ea..a7f44e1819e3f 100644
--- a/pandas/core/internals/construction.py
+++ b/pandas/core/internals/construction.py
@@ -193,7 +193,7 @@ def ndarray_to_mgr(
     values, index, columns, dtype: DtypeObj | None, copy: bool
 ) -> Manager:
     # used in DataFrame.__init__
-    # input must be a ndarray, list, Series, Index, ExtensionArray
+    # input must be an ndarray, list, Series, Index, ExtensionArray
     infer_object = not isinstance(values, (ABCSeries, Index, ExtensionArray))
 
     if isinstance(values, ABCSeries):
diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py
index b407342b85f9c..5e2050cd19767 100644
--- a/pandas/core/internals/managers.py
+++ b/pandas/core/internals/managers.py
@@ -807,7 +807,7 @@ def reindex_indexer(
         only_slice : bool, default False
             Whether to take views, not copies, along columns.
         use_na_proxy : bool, default False
-            Whether to use a np.void ndarray for newly introduced columns.
+            Whether to use an np.void ndarray for newly introduced columns.
 
         pandas-indexer with -1's only.
         """
@@ -883,7 +883,7 @@ def _slice_take_blocks_ax0(
             If True, we always return views on existing arrays, never copies.
             This is used when called from ops.blockwise.operate_blockwise.
         use_na_proxy : bool, default False
-            Whether to use a np.void ndarray for newly introduced columns.
+            Whether to use an np.void ndarray for newly introduced columns.
         ref_inplace_op: bool, default False
             Don't track refs if True because we operate inplace
 
diff --git a/pandas/core/missing.py b/pandas/core/missing.py
index 338dc4a0b8a08..1687fdb664963 100644
--- a/pandas/core/missing.py
+++ b/pandas/core/missing.py
@@ -851,7 +851,7 @@ def pad_or_backfill_inplace(
     # reshape a 1 dim if needed
     if values.ndim == 1:
         if axis != 0:  # pragma: no cover
-            raise AssertionError("cannot interpolate on a ndim == 1 with axis != 0")
+            raise AssertionError("cannot interpolate on an ndim == 1 with axis != 0")
         values = values.reshape(tuple((1,) + values.shape))
 
     method = clean_fill_method(method)
diff --git a/pandas/core/ops/mask_ops.py b/pandas/core/ops/mask_ops.py
index 86a93e5a3ca2b..03347804ba35c 100644
--- a/pandas/core/ops/mask_ops.py
+++ b/pandas/core/ops/mask_ops.py
@@ -49,7 +49,7 @@ def kleene_or(
         return kleene_or(right, left, right_mask, left_mask)
 
     if not isinstance(left, np.ndarray):
-        raise TypeError("Either `left` or `right` need to be a np.ndarray.")
+        raise TypeError("Either `left` or `right` need to be an np.ndarray.")
 
     raise_for_nan(right, method="or")
 
@@ -113,7 +113,7 @@ def kleene_xor(
         return kleene_xor(right, left, right_mask, left_mask)
 
     if not isinstance(left, np.ndarray):
-        raise TypeError("Either `left` or `right` need to be a np.ndarray.")
+        raise TypeError("Either `left` or `right` need to be an np.ndarray.")
 
     raise_for_nan(right, method="xor")
     if right is libmissing.NA:
@@ -163,7 +163,7 @@ def kleene_and(
         return kleene_and(right, left, right_mask, left_mask)
 
     if not isinstance(left, np.ndarray):
-        raise TypeError("Either `left` or `right` need to be a np.ndarray.")
+        raise TypeError("Either `left` or `right` need to be an np.ndarray.")
     raise_for_nan(right, method="and")
 
     if right is libmissing.NA:
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 110037e496751..08ea6827ca56e 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -4293,7 +4293,7 @@ def explode(self, ignore_index: bool = False) -> Series:
         This routine will explode list-likes including lists, tuples, sets,
         Series, and np.ndarray. The result dtype of the subset rows will
         be object. Scalars will be returned unchanged, and empty list-likes will
-        result in a np.nan for that row. In addition, the ordering of elements in
+        result in an np.nan for that row. In addition, the ordering of elements in
         the output will be non-deterministic when exploding sets.
 
         Reference :ref:`the user guide <reshaping.explode>` for more examples.
diff --git a/pandas/core/util/hashing.py b/pandas/core/util/hashing.py
index 3d2e65f79e43a..ae36a9a6176e9 100644
--- a/pandas/core/util/hashing.py
+++ b/pandas/core/util/hashing.py
@@ -270,7 +270,7 @@ def hash_array(
       dtype=uint64)
     """
     if not hasattr(vals, "dtype"):
-        raise TypeError("must pass a ndarray-like")
+        raise TypeError("must pass an ndarray-like")
 
     if isinstance(vals, ABCExtensionArray):
         return vals._hash_pandas_object(
diff --git a/pandas/errors/__init__.py b/pandas/errors/__init__.py
index 8c26744f171c3..f4864518ae5e6 100644
--- a/pandas/errors/__init__.py
+++ b/pandas/errors/__init__.py
@@ -991,7 +991,7 @@ class InvalidColumnName(Warning):
 
 class CategoricalConversionWarning(Warning):
     """
-    Warning is raised when reading a partial labeled Stata file using a iterator.
+    Warning is raised when reading a partial labeled Stata file using an iterator.
 
     This warning helps ensure data integrity and alerts users to potential issues
     during the incremental reading of Stata files with labeled data, allowing for
diff --git a/pandas/io/json/_json.py b/pandas/io/json/_json.py
index d226e81bc7511..985a192eb79f4 100644
--- a/pandas/io/json/_json.py
+++ b/pandas/io/json/_json.py
@@ -1280,7 +1280,7 @@ def _try_convert_data(
     @final
     def _try_convert_to_date(self, data: Series) -> Series:
         """
-        Try to parse a ndarray like into a date column.
+        Try to parse an ndarray like into a date column.
 
         Try to coerce object in epoch/iso formats and integer/float in epoch
         formats.
diff --git a/pandas/plotting/_matplotlib/core.py b/pandas/plotting/_matplotlib/core.py
index b8f59363b5107..0834501c4429d 100644
--- a/pandas/plotting/_matplotlib/core.py
+++ b/pandas/plotting/_matplotlib/core.py
@@ -1126,16 +1126,16 @@ def _parse_errorbars(
         Error bars can be specified in several ways:
             Series: the user provides a pandas.Series object of the same
                     length as the data
-            ndarray: provides a np.ndarray of the same length as the data
+            ndarray: provides an np.ndarray of the same length as the data
             DataFrame/dict: error values are paired with keys matching the
                     key in the plotted DataFrame
             str: the name of the column within the plotted DataFrame
 
         Asymmetrical error bars are also supported, however raw error values
-        must be provided in this case. For a ``N`` length :class:`Series`, a
+        must be provided in this case. For an ``N`` length :class:`Series`, a
         ``2xN`` array should be provided indicating lower and upper (or left
-        and right) errors. For a ``MxN`` :class:`DataFrame`, asymmetrical errors
-        should be in a ``Mx2xN`` array.
+        and right) errors. For an ``MxN`` :class:`DataFrame`, asymmetrical errors
+        should be in an ``Mx2xN`` array.
         """
         if err is None:
             return None, data
diff --git a/pandas/tests/arithmetic/test_timedelta64.py b/pandas/tests/arithmetic/test_timedelta64.py
index aee8c7b3989ba..8ba64bb5d2139 100644
--- a/pandas/tests/arithmetic/test_timedelta64.py
+++ b/pandas/tests/arithmetic/test_timedelta64.py
@@ -1830,7 +1830,7 @@ def test_td64_div_object_mixed_result(self, box_with_array):
             expected = expected.to_numpy()
         tm.assert_equal(res, expected)
         if box_with_array is DataFrame:
-            # We have a np.timedelta64(NaT), not pd.NaT
+            # We have an np.timedelta64(NaT), not pd.NaT
             assert isinstance(res.iloc[1, 0], np.timedelta64)
 
         res = tdi // other
@@ -1841,7 +1841,7 @@ def test_td64_div_object_mixed_result(self, box_with_array):
             expected = expected.to_numpy()
         tm.assert_equal(res, expected)
         if box_with_array is DataFrame:
-            # We have a np.timedelta64(NaT), not pd.NaT
+            # We have an np.timedelta64(NaT), not pd.NaT
             assert isinstance(res.iloc[1, 0], np.timedelta64)
 
     # ------------------------------------------------------------------
diff --git a/pandas/tests/arrays/boolean/test_logical.py b/pandas/tests/arrays/boolean/test_logical.py
index 97a24e0f24756..041a596b6faea 100644
--- a/pandas/tests/arrays/boolean/test_logical.py
+++ b/pandas/tests/arrays/boolean/test_logical.py
@@ -249,7 +249,7 @@ def test_no_masked_assumptions(self, other, all_logical_operators):
 
 @pytest.mark.parametrize("operation", [kleene_or, kleene_xor, kleene_and])
 def test_error_both_scalar(operation):
-    msg = r"Either `left` or `right` need to be a np\.ndarray."
+    msg = r"Either `left` or `right` need to be an np\.ndarray."
     with pytest.raises(TypeError, match=msg):
         # masks need to be non-None, otherwise it ends up in an infinite recursion
         operation(True, True, np.zeros(1), np.zeros(1))
diff --git a/pandas/tests/frame/test_arithmetic.py b/pandas/tests/frame/test_arithmetic.py
index 5e50759d34014..24996c39eff63 100644
--- a/pandas/tests/frame/test_arithmetic.py
+++ b/pandas/tests/frame/test_arithmetic.py
@@ -444,7 +444,7 @@ def test_flex_comparison_nat(self):
         df = DataFrame([pd.NaT])
 
         result = df == pd.NaT
-        # result.iloc[0, 0] is a np.bool_ object
+        # result.iloc[0, 0] is an np.bool_ object
         assert result.iloc[0, 0].item() is False
 
         result = df.eq(pd.NaT)
diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py
index 896a795adac43..611821361af12 100644
--- a/pandas/tests/frame/test_constructors.py
+++ b/pandas/tests/frame/test_constructors.py
@@ -1869,7 +1869,7 @@ def test_constructor_with_datetimes(self, using_infer_string):
         )
         tm.assert_series_equal(result, expected)
 
-        # check with ndarray construction ndim==0 (e.g. we are passing a ndim 0
+        # check with ndarray construction ndim==0 (e.g. we are passing an ndim 0
         # ndarray with a dtype specified)
         df = DataFrame(
             {
diff --git a/pandas/tests/indexes/multi/test_constructors.py b/pandas/tests/indexes/multi/test_constructors.py
index 6b461fcf3920d..a76b3443a079e 100644
--- a/pandas/tests/indexes/multi/test_constructors.py
+++ b/pandas/tests/indexes/multi/test_constructors.py
@@ -762,7 +762,7 @@ def test_index_equal_empty_iterable():
 
 
 def test_raise_invalid_sortorder():
-    # Test that the MultiIndex constructor raise when a incorrect sortorder is given
+    # Test that the MultiIndex constructor raise when an incorrect sortorder is given
     # GH#28518
 
     levels = [[0, 1], [0, 1, 2]]
diff --git a/pandas/tests/indexing/test_loc.py b/pandas/tests/indexing/test_loc.py
index 1877746aa9c97..3678819216c38 100644
--- a/pandas/tests/indexing/test_loc.py
+++ b/pandas/tests/indexing/test_loc.py
@@ -333,7 +333,7 @@ def test_loc_getitem_missing_unicode_key(self):
 
     def test_loc_getitem_dups(self):
         # GH 5678
-        # repeated getitems on a dup index returning a ndarray
+        # repeated getitems on a dup index returning an ndarray
         df = DataFrame(
             np.random.default_rng(2).random((20, 5)),
             index=["ABCDE"[x % 5] for x in range(20)],
@@ -1431,7 +1431,7 @@ def test_loc_setitem_listlike_with_timedelta64index(self, indexer, expected):
         tm.assert_frame_equal(expected, df)
 
     def test_loc_setitem_categorical_values_partial_column_slice(self):
-        # Assigning a Category to parts of a int/... column uses the values of
+        # Assigning a Category to parts of an int/... column uses the values of
         # the Categorical
         df = DataFrame({"a": [1, 1, 1, 1, 1], "b": list("aaaaa")})
         with pytest.raises(TypeError, match="Invalid value"):
diff --git a/pandas/tests/io/pytables/test_select.py b/pandas/tests/io/pytables/test_select.py
index 0dffb284fa6d2..c115311f1df66 100644
--- a/pandas/tests/io/pytables/test_select.py
+++ b/pandas/tests/io/pytables/test_select.py
@@ -251,7 +251,7 @@ def test_select_dtypes(setup_path, request):
         tm.assert_frame_equal(expected, result)
 
         # https://github.com/PyTables/PyTables/issues/282
-        # bug in selection when 0th row has a np.nan and an index
+        # bug in selection when 0th row has an np.nan and an index
         # store.append('df3',df,data_columns=True)
         # result = store.select(
         #    'df3', where='values>2.0')
diff --git a/pandas/tests/reductions/test_reductions.py b/pandas/tests/reductions/test_reductions.py
index db27572b9da26..c4fe681de7cea 100644
--- a/pandas/tests/reductions/test_reductions.py
+++ b/pandas/tests/reductions/test_reductions.py
@@ -229,7 +229,7 @@ def test_nan_int_timedelta_sum(self):
 
 class TestIndexReductions:
     # Note: the name TestIndexReductions indicates these tests
-    #  were moved from a Index-specific test file, _not_ that these tests are
+    #  were moved from an Index-specific test file, _not_ that these tests are
     #  intended long-term to be Index-specific
 
     @pytest.mark.parametrize(
diff --git a/pandas/tests/reshape/concat/test_invalid.py b/pandas/tests/reshape/concat/test_invalid.py
index b1c44fc0debc3..3910dfa38a4bc 100644
--- a/pandas/tests/reshape/concat/test_invalid.py
+++ b/pandas/tests/reshape/concat/test_invalid.py
@@ -14,7 +14,7 @@
 class TestInvalidConcat:
     @pytest.mark.parametrize("obj", [1, {}, [1, 2], (1, 2)])
     def test_concat_invalid(self, obj):
-        # trying to concat a ndframe with a non-ndframe
+        # trying to concat an ndframe with a non-ndframe
         df1 = DataFrame(range(2))
         msg = (
             f"cannot concatenate object of type '{type(obj)}'; "
diff --git a/pandas/tests/reshape/test_from_dummies.py b/pandas/tests/reshape/test_from_dummies.py
index cce5a490b4839..0997baf7c3f74 100644
--- a/pandas/tests/reshape/test_from_dummies.py
+++ b/pandas/tests/reshape/test_from_dummies.py
@@ -304,7 +304,7 @@ def test_no_prefix_string_cats_contains_get_dummies_NaN_column():
         pytest.param(
             1,
             {"": ["a", "b", 1]},
-            id="default_category is a int",
+            id="default_category is an int",
         ),
         pytest.param(
             1.25,
diff --git a/pandas/tests/series/methods/test_rank.py b/pandas/tests/series/methods/test_rank.py
index 357894cbd0fe3..0102bc811de9b 100644
--- a/pandas/tests/series/methods/test_rank.py
+++ b/pandas/tests/series/methods/test_rank.py
@@ -92,7 +92,7 @@ def test_rank(self, datetime_series):
         mask = np.isnan(datetime_series)
         filled = datetime_series.fillna(np.inf)
 
-        # rankdata returns a ndarray
+        # rankdata returns an ndarray
         exp = Series(sp_stats.rankdata(filled), index=filled.index, name="ts")
         exp[mask] = np.nan
 
diff --git a/pandas/tests/tools/test_to_datetime.py b/pandas/tests/tools/test_to_datetime.py
index 67b1e8668e5f6..4435a6d9e28ab 100644
--- a/pandas/tests/tools/test_to_datetime.py
+++ b/pandas/tests/tools/test_to_datetime.py
@@ -673,7 +673,7 @@ def test_to_datetime_mixed_datetime_and_string_with_format(
         # https://github.com/pandas-dev/pandas/issues/49298
         # https://github.com/pandas-dev/pandas/issues/50254
         # note: ISO8601 formats go down a fastpath, so we need to check both
-        # a ISO8601 format and a non-ISO8601 one
+        # an ISO8601 format and a non-ISO8601 one
         ts1 = constructor(args[0])
         ts2 = args[1]
         result = to_datetime([ts1, ts2], format=fmt, utc=utc)
@@ -697,7 +697,7 @@ def test_to_datetime_mixed_dt_and_str_with_format_mixed_offsets_utc_false_remove
         # https://github.com/pandas-dev/pandas/issues/50254
         # GH#57275
         # note: ISO8601 formats go down a fastpath, so we need to check both
-        # a ISO8601 format and a non-ISO8601 one
+        # an ISO8601 format and a non-ISO8601 one
         args = ["2000-01-01 01:00:00", "2000-01-01 02:00:00+00:00"]
         ts1 = constructor(args[0])
         ts2 = args[1]
@@ -796,7 +796,7 @@ def test_to_datetime_mixed_datetime_and_string_with_format_raises(
     ):
         # https://github.com/pandas-dev/pandas/issues/49298
         # note: ISO8601 formats go down a fastpath, so we need to check both
-        # a ISO8601 format and a non-ISO8601 one
+        # an ISO8601 format and a non-ISO8601 one
         ts1 = constructor(args[0])
         ts2 = constructor(args[1])
         with pytest.raises(
diff --git a/pandas/tests/util/test_hashing.py b/pandas/tests/util/test_hashing.py
index e654534ccd453..d6bc7017c2483 100644
--- a/pandas/tests/util/test_hashing.py
+++ b/pandas/tests/util/test_hashing.py
@@ -68,7 +68,7 @@ def test_hash_array_mixed(dtype):
 
 @pytest.mark.parametrize("val", [5, "foo", pd.Timestamp("20130101")])
 def test_hash_array_errors(val):
-    msg = "must pass a ndarray-like"
+    msg = "must pass an ndarray-like"
     with pytest.raises(TypeError, match=msg):
         hash_array(val)