diff --git a/pandas/core/aggregation.py b/pandas/core/aggregation.py
index f6380808d5ac2..6130e05b2a4dc 100644
--- a/pandas/core/aggregation.py
+++ b/pandas/core/aggregation.py
@@ -89,7 +89,7 @@ def normalize_keyword_aggregation(kwargs: dict) -> Tuple[dict, List[str], List[i
     ]
     uniquified_aggspec = _make_unique_kwarg_list(aggspec_order)
 
-    # get the new indice of columns by comparison
+    # get the new index of columns by comparison
     col_idx_order = Index(uniquified_aggspec).get_indexer(uniquified_order)
     return aggspec, columns, col_idx_order
 
@@ -182,7 +182,7 @@ def maybe_mangle_lambdas(agg_spec: Any) -> Any:
     is_dict = is_dict_like(agg_spec)
     if not (is_dict or is_list_like(agg_spec)):
         return agg_spec
-    mangled_aggspec = type(agg_spec)()  # dict or OrderdDict
+    mangled_aggspec = type(agg_spec)()  # dict or OrderedDict
 
     if is_dict:
         for key, aggfuncs in agg_spec.items():
diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index c2115094918e5..b97063cfa7fd0 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -125,7 +125,7 @@ def _ensure_data(
 
     except (TypeError, ValueError, OverflowError):
         # if we are trying to coerce to a dtype
-        # and it is incompat this will fall through to here
+        # and it is incompatible this will fall through to here
         return ensure_object(values), np.dtype("object")
 
     # datetimelike
@@ -473,7 +473,7 @@ def _factorize_array(
     values : ndarray
     na_sentinel : int, default -1
     size_hint : int, optional
-        Passsed through to the hashtable's 'get_labels' method
+        Passed through to the hashtable's 'get_labels' method
     na_value : object, optional
         A value in `values` to consider missing. Note: only use this
         parameter when you know that you don't have any values pandas would
@@ -1239,7 +1239,7 @@ def get_indexer(current_indexer, other_indexer):
                 break
 
             # Now find all values which are equal to
-            # the (nsmallest: largest)/(nlarrgest: smallest)
+            # the (nsmallest: largest)/(nlargest: smallest)
             # from our series.
             border_value = values == values[values.index[-1]]
 
diff --git a/pandas/core/arrays/base.py b/pandas/core/arrays/base.py
index bd903d9b1fae3..92b84907e1c11 100644
--- a/pandas/core/arrays/base.py
+++ b/pandas/core/arrays/base.py
@@ -696,7 +696,7 @@ def _values_for_factorize(self) -> Tuple[np.ndarray, Any]:
         na_value : object
             The value in `values` to consider missing. This will be treated
             as NA in the factorization routines, so it will be coded as
-            `na_sentinal` and not included in `uniques`. By default,
+            `na_sentinel` and not included in `uniques`. By default,
             ``np.nan`` is used.
 
         Notes
diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index 59ae2dc171cf8..dc807f467f65e 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -2015,7 +2015,7 @@ def __setitem__(self, key, value):
         # tuple of indexers (dataframe)
         elif isinstance(key, tuple):
             # only allow 1 dimensional slicing, but can
-            # in a 2-d case be passd (slice(None),....)
+            # in a 2-d case be passed (slice(None),....)
             if len(key) == 2:
                 if not com.is_null_slice(key[0]):
                     raise AssertionError("invalid slicing for a 1-ndim categorical")
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index a741c32a1035a..9ee150447eb5f 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -198,7 +198,7 @@ def _check_compatible_with(
         ----------
         other
         setitem : bool, default False
-            For __setitem__ we may have stricter compatibility resrictions than
+            For __setitem__ we may have stricter compatibility restrictions than
             for comparisons.
 
         Raises
diff --git a/pandas/core/arrays/period.py b/pandas/core/arrays/period.py
index d04fdee8961e4..8be714820d18b 100644
--- a/pandas/core/arrays/period.py
+++ b/pandas/core/arrays/period.py
@@ -284,7 +284,7 @@ def __array__(self, dtype=None) -> np.ndarray:
         elif dtype == bool:
             return ~self._isnan
 
-        # This will raise TypeErorr for non-object dtypes
+        # This will raise TypeError for non-object dtypes
         return np.array(list(self), dtype=object)
 
     def __arrow_array__(self, type=None):
diff --git a/pandas/core/arrays/sparse/array.py b/pandas/core/arrays/sparse/array.py
index 0877e98e55311..9b6d4ad7323d0 100644
--- a/pandas/core/arrays/sparse/array.py
+++ b/pandas/core/arrays/sparse/array.py
@@ -784,7 +784,7 @@ def __getitem__(self, key):
             # TODO: I think we can avoid densifying when masking a
             # boolean SparseArray with another. Need to look at the
             # key's fill_value for True / False, and then do an intersection
-            # on the indicies of the sp_values.
+            # on the indices of the sp_values.
             if isinstance(key, SparseArray):
                 if is_bool_dtype(key):
                     key = key.to_dense()
diff --git a/pandas/core/arrays/timedeltas.py b/pandas/core/arrays/timedeltas.py
index a460d07e1f6f2..a62f94b1a3665 100644
--- a/pandas/core/arrays/timedeltas.py
+++ b/pandas/core/arrays/timedeltas.py
@@ -258,7 +258,7 @@ def _generate_range(cls, start, end, periods, freq, closed=None):
         if start is None and end is None:
             if closed is not None:
                 raise ValueError(
-                    "Closed has to be None if not both of startand end are defined"
+                    "Closed has to be None if not both of start and end are defined"
                 )
 
         left_closed, right_closed = dtl.validate_endpoints(closed)
@@ -877,7 +877,7 @@ def sequence_to_td64ns(data, copy=False, unit="ns", errors="raise"):
     """
     Parameters
     ----------
-    array : list-like
+    data : list-like
     copy : bool, default False
     unit : str, default "ns"
         The timedelta unit to treat integers as multiples of.
@@ -930,7 +930,7 @@ def sequence_to_td64ns(data, copy=False, unit="ns", errors="raise"):
         copy = copy and not copy_made
 
     elif is_float_dtype(data.dtype):
-        # cast the unit, multiply base/frace separately
+        # cast the unit, multiply base/frac separately
         # to avoid precision issues from float -> int
         mask = np.isnan(data)
         m, p = precision_from_unit(unit)
diff --git a/pandas/core/config_init.py b/pandas/core/config_init.py
index 4d5b9c6920e48..5089445c79897 100644
--- a/pandas/core/config_init.py
+++ b/pandas/core/config_init.py
@@ -604,7 +604,7 @@ def use_inf_as_na_cb(key):
 : str
     The plotting backend to use. The default value is "matplotlib", the
     backend provided with pandas. Other backends can be specified by
-    prodiving the name of the module that implements the backend.
+    providing the name of the module that implements the backend.
 """
 
 
diff --git a/pandas/core/construction.py b/pandas/core/construction.py
index e6e26f0eec597..b110a316a76d9 100644
--- a/pandas/core/construction.py
+++ b/pandas/core/construction.py
@@ -543,7 +543,7 @@ def _try_cast(
         return subarr
 
     try:
-        # GH#15832: Check if we are requesting a numeric dype and
+        # GH#15832: Check if we are requesting a numeric dtype and
         # that we can convert the data to the requested dtype.
         if is_integer_dtype(dtype):
             # this will raise if we have e.g. floats
diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index b91cfde45f079..9865a7d28542d 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -198,8 +198,7 @@ def maybe_downcast_numeric(result, dtype, do_round: bool = False):
         return result
 
     if isinstance(result, list):
-        # reached via groupoby.agg _ohlc; really this should be handled
-        #  earlier
+        # reached via groupby.agg._ohlc; really this should be handled earlier
         result = np.array(result)
 
     def trans(x):
@@ -1693,7 +1692,7 @@ def convert_scalar_for_putitemlike(scalar, dtype: np.dtype):
     Parameters
     ----------
     scalar : scalar
-    dtype : np.dtpye
+    dtype : np.dtype
 
     Returns
     -------
diff --git a/pandas/core/dtypes/concat.py b/pandas/core/dtypes/concat.py
index 82b2795582ff1..e7e8d016e52b2 100644
--- a/pandas/core/dtypes/concat.py
+++ b/pandas/core/dtypes/concat.py
@@ -357,7 +357,7 @@ def _concatenate_2d(to_concat, axis: int):
 def concat_datetime(to_concat, axis=0, typs=None):
     """
     provide concatenation of an datetimelike array of arrays each of which is a
-    single M8[ns], datetimet64[ns, tz] or m8[ns] dtype
+    single M8[ns], datetime64[ns, tz] or m8[ns] dtype
 
     Parameters
     ----------
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 4e86b3710a1bd..1203cd9fbd1b3 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -644,7 +644,7 @@ def _repr_fits_horizontal_(self, ignore_width: bool = False) -> bool:
 
         In case of non-interactive session, no boundaries apply.
 
-        `ignore_width` is here so ipnb+HTML output can behave the way
+        `ignore_width` is here so ipynb+HTML output can behave the way
         users expect. display.max_columns remains in effect.
         GH3541, GH3573
         """
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index af5930fd22869..438babda9ff7a 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -1555,7 +1555,7 @@ def filter(self, func, dropna=True, *args, **kwargs):
 
         Parameters
         ----------
-        f : function
+        func : function
             Function to apply to each subframe. Should return True or False.
         dropna : Drop groups that do not pass the filter. True by default;
             If False, groups that evaluate False are filled with NaNs.
diff --git a/pandas/core/groupby/ops.py b/pandas/core/groupby/ops.py
index 71d7a07aadf7f..43e6b02e9dc53 100644
--- a/pandas/core/groupby/ops.py
+++ b/pandas/core/groupby/ops.py
@@ -405,8 +405,8 @@ def _get_cython_func_and_vals(
 
         Parameters
         ----------
-        kind : sttr
-        how : srt
+        kind : str
+        how : str
         values : np.ndarray
         is_numeric : bool
 
@@ -643,7 +643,7 @@ def agg_series(
             return self._aggregate_series_pure_python(obj, func)
 
         elif obj.index._has_complex_internals:
-            # Pre-empt TypeError in _aggregate_series_fast
+            # Preempt TypeError in _aggregate_series_fast
             return self._aggregate_series_pure_python(obj, func)
 
         try:
@@ -895,7 +895,7 @@ def agg_series(
         assert len(self.bins) > 0  # otherwise we'd get IndexError in get_result
 
         if is_extension_array_dtype(obj.dtype):
-            # pre-empt SeriesBinGrouper from raising TypeError
+            # preempt SeriesBinGrouper from raising TypeError
             return self._aggregate_series_pure_python(obj, func)
 
         dummy = obj[:0]
diff --git a/pandas/core/indexers.py b/pandas/core/indexers.py
index 3d0e3699264a8..6dbcfef46fa98 100644
--- a/pandas/core/indexers.py
+++ b/pandas/core/indexers.py
@@ -441,7 +441,7 @@ def check_array_indexer(array: AnyArrayLike, indexer: Any) -> Any:
     """
     from pandas.core.construction import array as pd_array
 
-    # whathever is not an array-like is returned as-is (possible valid array
+    # whatever is not an array-like is returned as-is (possible valid array
     # indexers that are not array-like: integer, slice, Ellipsis, None)
     # In this context, tuples are not considered as array-like, as they have
     # a specific meaning in indexing (multi-dimensional indexing)
diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py
index 72369a13b150f..f1e1ebcaca1c4 100644
--- a/pandas/core/indexes/multi.py
+++ b/pandas/core/indexes/multi.py
@@ -2784,7 +2784,7 @@ def get_loc_level(self, key, level=0, drop_level: bool = True):
         def maybe_mi_droplevels(indexer, levels, drop_level: bool):
             if not drop_level:
                 return self[indexer]
-            # kludgearound
+            # kludge around
             orig_index = new_index = self[indexer]
             levels = [self._get_level_number(i) for i in levels]
             for i in sorted(levels, reverse=True):
diff --git a/pandas/core/indexes/period.py b/pandas/core/indexes/period.py
index 54892d5656990..b0b85f69396ba 100644
--- a/pandas/core/indexes/period.py
+++ b/pandas/core/indexes/period.py
@@ -473,7 +473,7 @@ def get_loc(self, key, method=None, tolerance=None):
         Parameters
         ----------
         key : Period, NaT, str, or datetime
-            String or datetime key must be parseable as Period.
+            String or datetime key must be parsable as Period.
 
         Returns
         -------
diff --git a/pandas/core/indexes/range.py b/pandas/core/indexes/range.py
index b463b8d738d30..c34b8965ca36a 100644
--- a/pandas/core/indexes/range.py
+++ b/pandas/core/indexes/range.py
@@ -741,7 +741,7 @@ def _make_evaluate_binop(op, step=False):
             """
             Parameters
             ----------
-            op : callable that accepts 2 parms
+            op : callable that accepts 2 params
                 perform the binary op
             step : callable, optional, default to False
                 op to apply to the step parm if not None
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index e51ec33ba8519..b857a59195695 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -90,7 +90,7 @@ class IndexingError(Exception):
 
 class IndexingMixin:
     """
-    Mixin for adding .loc/.iloc/.at/.iat to Datafames and Series.
+    Mixin for adding .loc/.iloc/.at/.iat to Dataframes and Series.
     """
 
     @property
@@ -1498,7 +1498,7 @@ def _convert_to_indexer(self, key, axis: int, is_setter: bool = False):
         return key
 
     def _get_setitem_indexer(self, key):
-        # GH#32257 Fall through to let numnpy do validation
+        # GH#32257 Fall through to let numpy do validation
         return key
 
     # -------------------------------------------------------------------
@@ -2257,9 +2257,9 @@ def need_slice(obj) -> bool:
 
 def _non_reducing_slice(slice_):
     """
-    Ensurse that a slice doesn't reduce to a Series or Scalar.
+    Ensure that a slice doesn't reduce to a Series or Scalar.
 
-    Any user-paseed `subset` should have this called on it
+    Any user-passed `subset` should have this called on it
     to make sure we're always working with DataFrames.
     """
     # default to column slice, like DataFrame
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index e4dcffae45f67..67512114ec5b1 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -825,7 +825,7 @@ def setitem(self, indexer, value):
 
             return self.astype(dtype).setitem(indexer, value)
 
-        # value must be storeable at this moment
+        # value must be storable at this moment
         if is_extension_array_dtype(getattr(value, "dtype", None)):
             # We need to be careful not to allow through strings that
             #  can be parsed to EADtypes
diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py
index 5ab792563f136..3b88edabe9eb0 100644
--- a/pandas/core/internals/managers.py
+++ b/pandas/core/internals/managers.py
@@ -600,7 +600,7 @@ def replace_list(
         """ do a list replace """
         inplace = validate_bool_kwarg(inplace, "inplace")
 
-        # figure out our mask a-priori to avoid repeated replacements
+        # figure out our mask apriori to avoid repeated replacements
         values = self.as_array()
 
         def comp(s, regex=False):
diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index bab9df0b70598..9f4541e0917b9 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -143,7 +143,7 @@ def _bn_ok_dtype(dtype: DtypeObj, name: str) -> bool:
 
         # GH 9422
         # further we also want to preserve NaN when all elements
-        # are NaN, unlinke bottleneck/numpy which consider this
+        # are NaN, unlike bottleneck/numpy which consider this
         # to be 0
         if name in ["nansum", "nanprod"]:
             return False
diff --git a/pandas/core/ops/array_ops.py b/pandas/core/ops/array_ops.py
index a1d853e38e757..59ac2a2071f0a 100644
--- a/pandas/core/ops/array_ops.py
+++ b/pandas/core/ops/array_ops.py
@@ -131,7 +131,7 @@ def na_arithmetic_op(left, right, op, str_rep: Optional[str], is_cmp: bool = Fal
     """
     Return the result of evaluating op on the passed in values.
 
-    If native types are not compatible, try coersion to object dtype.
+    If native types are not compatible, try coercion to object dtype.
 
     Parameters
     ----------
@@ -184,7 +184,7 @@ def arithmetic_op(left: ArrayLike, right: Any, op, str_rep: str):
 
     Returns
     -------
-    ndarrray or ExtensionArray
+    ndarray or ExtensionArray
         Or a 2-tuple of these in the case of divmod or rdivmod.
     """
 
@@ -315,7 +315,7 @@ def logical_op(left: ArrayLike, right: Any, op) -> ArrayLike:
 
     Returns
     -------
-    ndarrray or ExtensionArray
+    ndarray or ExtensionArray
     """
     fill_int = lambda x: x
 
diff --git a/pandas/core/reshape/merge.py b/pandas/core/reshape/merge.py
index e3f4a80ecce7c..bc612f891d362 100644
--- a/pandas/core/reshape/merge.py
+++ b/pandas/core/reshape/merge.py
@@ -652,7 +652,7 @@ def __init__(
         ) = self._get_merge_keys()
 
         # validate the merge keys dtypes. We may need to coerce
-        # to avoid incompat dtypes
+        # to avoid incompatible dtypes
         self._maybe_coerce_merge_keys()
 
         # If argument passed to validate,
@@ -1067,7 +1067,7 @@ def _get_merge_keys(self):
         return left_keys, right_keys, join_names
 
     def _maybe_coerce_merge_keys(self):
-        # we have valid mergees but we may have to further
+        # we have valid merges but we may have to further
         # coerce these if they are originally incompatible types
         #
         # for example if these are categorical, but are not dtype_equal
@@ -1392,7 +1392,7 @@ def _restore_dropped_levels_multijoin(
 
     """
 
-    def _convert_to_mulitindex(index) -> MultiIndex:
+    def _convert_to_multiindex(index) -> MultiIndex:
         if isinstance(index, MultiIndex):
             return index
         else:
@@ -1402,7 +1402,7 @@ def _convert_to_mulitindex(index) -> MultiIndex:
     # the returned index if of type Index
     # Assure that join_index is of type MultiIndex
     # so that dropped levels can be appended
-    join_index = _convert_to_mulitindex(join_index)
+    join_index = _convert_to_multiindex(join_index)
 
     join_levels = join_index.levels
     join_codes = join_index.codes
diff --git a/pandas/core/reshape/pivot.py b/pandas/core/reshape/pivot.py
index 17473ac26dfd6..c8d5eecf0e496 100644
--- a/pandas/core/reshape/pivot.py
+++ b/pandas/core/reshape/pivot.py
@@ -210,7 +210,7 @@ def _add_margins(
     grand_margin = _compute_grand_margin(data, values, aggfunc, margins_name)
 
     if table.ndim == 2:
-        # i.e. DataFramae
+        # i.e. DataFrame
         for level in table.columns.names[1:]:
             if margins_name in table.columns.get_level_values(level):
                 raise ValueError(msg)
diff --git a/pandas/core/reshape/tile.py b/pandas/core/reshape/tile.py
index 345239eeb2372..6eae54633befb 100644
--- a/pandas/core/reshape/tile.py
+++ b/pandas/core/reshape/tile.py
@@ -440,7 +440,7 @@ def _bins_to_cuts(
                 categories=labels if len(set(labels)) == len(labels) else None,
                 ordered=ordered,
             )
-        # TODO: handle mismach between categorical label order and pandas.cut order.
+        # TODO: handle mismatch between categorical label order and pandas.cut order.
         np.putmask(ids, na_mask, 0)
         result = algos.take_nd(labels, ids - 1)
 
diff --git a/pandas/core/series.py b/pandas/core/series.py
index eb409b432f89c..64220862c3e51 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -912,7 +912,7 @@ def __getitem__(self, key):
     def _get_with(self, key):
         # other: fancy integer or otherwise
         if isinstance(key, slice):
-            # _convert_slice_indexer to determin if this slice is positional
+            # _convert_slice_indexer to determine if this slice is positional
             #  or label based, and if the latter, convert to positional
             slobj = self.index._convert_slice_indexer(key, kind="getitem")
             return self._slice(slobj)
@@ -3377,7 +3377,7 @@ def nlargest(self, n=5, keep="first") -> "Series":
         ...                         "Malta": 434000, "Maldives": 434000,
         ...                         "Brunei": 434000, "Iceland": 337000,
         ...                         "Nauru": 11300, "Tuvalu": 11300,
-        ...                         "Anguilla": 11300, "Monserat": 5200}
+        ...                         "Anguilla": 11300, "Montserrat": 5200}
         >>> s = pd.Series(countries_population)
         >>> s
         Italy       59000000
@@ -3389,7 +3389,7 @@ def nlargest(self, n=5, keep="first") -> "Series":
         Nauru          11300
         Tuvalu         11300
         Anguilla       11300
-        Monserat        5200
+        Montserrat      5200
         dtype: int64
 
         The `n` largest elements where ``n=5`` by default.
@@ -3475,7 +3475,7 @@ def nsmallest(self, n=5, keep="first") -> "Series":
         ...                         "Brunei": 434000, "Malta": 434000,
         ...                         "Maldives": 434000, "Iceland": 337000,
         ...                         "Nauru": 11300, "Tuvalu": 11300,
-        ...                         "Anguilla": 11300, "Monserat": 5200}
+        ...                         "Anguilla": 11300, "Montserrat": 5200}
         >>> s = pd.Series(countries_population)
         >>> s
         Italy       59000000
@@ -3487,13 +3487,13 @@ def nsmallest(self, n=5, keep="first") -> "Series":
         Nauru          11300
         Tuvalu         11300
         Anguilla       11300
-        Monserat        5200
+        Montserrat      5200
         dtype: int64
 
         The `n` smallest elements where ``n=5`` by default.
 
         >>> s.nsmallest()
-        Monserat      5200
+        Montserrat    5200
         Nauru        11300
         Tuvalu       11300
         Anguilla     11300
@@ -3504,7 +3504,7 @@ def nsmallest(self, n=5, keep="first") -> "Series":
         'first' so Nauru and Tuvalu will be kept.
 
         >>> s.nsmallest(3)
-        Monserat     5200
+        Montserrat   5200
         Nauru       11300
         Tuvalu      11300
         dtype: int64
@@ -3514,7 +3514,7 @@ def nsmallest(self, n=5, keep="first") -> "Series":
         with value 11300 based on the index order.
 
         >>> s.nsmallest(3, keep='last')
-        Monserat     5200
+        Montserrat   5200
         Anguilla    11300
         Tuvalu      11300
         dtype: int64
@@ -3523,7 +3523,7 @@ def nsmallest(self, n=5, keep="first") -> "Series":
         that the returned Series has four elements due to the three duplicates.
 
         >>> s.nsmallest(3, keep='all')
-        Monserat     5200
+        Montserrat   5200
         Nauru       11300
         Tuvalu      11300
         Anguilla    11300
diff --git a/pandas/core/strings.py b/pandas/core/strings.py
index 72d778524a364..1bb095d9bf72c 100644
--- a/pandas/core/strings.py
+++ b/pandas/core/strings.py
@@ -134,7 +134,7 @@ def _map_stringarray(
     func: Callable[[str], Any], arr: "StringArray", na_value: Any, dtype: Dtype
 ) -> ArrayLike:
     """
-    Map a callable over valid elements of a StringArrray.
+    Map a callable over valid elements of a StringArray.
 
     Parameters
     ----------
@@ -2008,11 +2008,11 @@ def _noarg_wrapper(
     docstring=None,
     forbidden_types=["bytes"],
     returns_string=True,
-    **kargs,
+    **kwargs,
 ):
     @forbid_nonstring_types(forbidden_types, name=name)
     def wrapper(self):
-        result = _na_map(f, self._parent, **kargs)
+        result = _na_map(f, self._parent, **kwargs)
         return self._wrap_result(result, returns_string=returns_string)
 
     wrapper.__name__ = f.__name__ if name is None else name
@@ -2321,7 +2321,7 @@ def _get_series_list(self, others):
             elif all(not is_list_like(x) for x in others):
                 return [Series(others, index=idx)]
         raise TypeError(
-            "others must be Series, Index, DataFrame, np.ndarrary "
+            "others must be Series, Index, DataFrame, np.ndarray "
             "or list-like (either containing only strings or "
             "containing only objects of type Series/Index/"
             "np.ndarray[1-dim])"
diff --git a/pandas/io/clipboard/__init__.py b/pandas/io/clipboard/__init__.py
index f4bd14ad5c679..40bff5a75709b 100644
--- a/pandas/io/clipboard/__init__.py
+++ b/pandas/io/clipboard/__init__.py
@@ -494,7 +494,7 @@ def paste_wsl():
 
 
 # Automatic detection of clipboard mechanisms
-# and importing is done in deteremine_clipboard():
+# and importing is done in determine_clipboard():
 def determine_clipboard():
     """
     Determine the OS/platform and set the copy() and paste() functions
diff --git a/pandas/io/formats/info.py b/pandas/io/formats/info.py
index d68a1fdde8da9..d2d5fdc7ab8a2 100644
--- a/pandas/io/formats/info.py
+++ b/pandas/io/formats/info.py
@@ -180,7 +180,7 @@ def _sizeof_fmt(num, size_qualifier):
 
     if verbose:
         _verbose_repr()
-    elif verbose is False:  # specifically set to False, not nesc None
+    elif verbose is False:  # specifically set to False, not necessarily None
         _non_verbose_repr()
     else:
         if exceeds_info_cols:
diff --git a/pandas/io/formats/style.py b/pandas/io/formats/style.py
index fecdf3b758f0f..f7ba4750bc2ad 100644
--- a/pandas/io/formats/style.py
+++ b/pandas/io/formats/style.py
@@ -124,7 +124,7 @@ class Styler:
     * Column label cells include
       * ``col_heading``
       * ``col<n>`` where `n` is the numeric position of the column
-      * ``evel<k>`` where `k` is the level in a MultiIndex
+      * ``level<k>`` where `k` is the level in a MultiIndex
 
     * Blank cells include ``blank``
     * Data cells include ``data``
@@ -542,7 +542,7 @@ def render(self, **kwargs) -> str:
         d = self._translate()
         # filter out empty styles, every cell will have a class
         # but the list of props may just be [['', '']].
-        # so we have the neested anys below
+        # so we have the nested anys below
         trimmed = [x for x in d["cellstyle"] if any(any(y) for y in x["props"])]
         d["cellstyle"] = trimmed
         d.update(kwargs)
diff --git a/pandas/io/json/_normalize.py b/pandas/io/json/_normalize.py
index e833fdc20d542..44765dbe74b46 100644
--- a/pandas/io/json/_normalize.py
+++ b/pandas/io/json/_normalize.py
@@ -241,7 +241,7 @@ def _pull_field(
 
     def _pull_records(js: Dict[str, Any], spec: Union[List, str]) -> List:
         """
-        Interal function to pull field for records, and similar to
+        Internal function to pull field for records, and similar to
         _pull_field, but require to return list. And will raise error
         if has non iterable value.
         """
diff --git a/pandas/io/parquet.py b/pandas/io/parquet.py
index 0a9daea105b64..cde7a98eb42ae 100644
--- a/pandas/io/parquet.py
+++ b/pandas/io/parquet.py
@@ -105,7 +105,7 @@ def write(
 
         table = self.api.Table.from_pandas(df, **from_pandas_kwargs)
         # write_to_dataset does not support a file-like object when
-        # a dircetory path is used, so just pass the path string.
+        # a directory path is used, so just pass the path string.
         if partition_cols is not None:
             self.api.parquet.write_to_dataset(
                 table,
@@ -190,7 +190,7 @@ def read(self, path, columns=None, **kwargs):
 
             # When path is s3:// an S3File is returned.
             # We need to retain the original path(str) while also
-            # pass the S3File().open function to fsatparquet impl.
+            # pass the S3File().open function to fastparquet impl.
             s3, filesystem = get_file_and_filesystem(path)
             try:
                 parquet_file = self.api.ParquetFile(path, open_with=filesystem.open)
diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py
index 82380d456cd6d..9b3a29026448e 100644
--- a/pandas/io/pytables.py
+++ b/pandas/io/pytables.py
@@ -2626,7 +2626,7 @@ class GenericFixed(Fixed):
     _reverse_index_map = {v: k for k, v in _index_type_map.items()}
     attributes: List[str] = []
 
-    # indexer helpders
+    # indexer helpers
     def _class_to_alias(self, cls) -> str:
         return self._index_type_map.get(cls, "")
 
@@ -2819,7 +2819,7 @@ def read_index_node(
     ) -> Index:
         data = node[start:stop]
         # If the index was an empty array write_array_empty() will
-        # have written a sentinel. Here we relace it with the original.
+        # have written a sentinel. Here we replace it with the original.
         if "shape" in node._v_attrs and np.prod(node._v_attrs.shape) == 0:
             data = np.empty(node._v_attrs.shape, dtype=node._v_attrs.value_type,)
         kind = _ensure_decoded(node._v_attrs.kind)
@@ -3592,7 +3592,7 @@ def validate_data_columns(self, data_columns, min_itemsize, non_index_axes):
             )
 
         # evaluate the passed data_columns, True == use all columns
-        # take only valide axis labels
+        # take only valid axis labels
         if data_columns is True:
             data_columns = list(axis_labels)
         elif data_columns is None:
diff --git a/pandas/io/stata.py b/pandas/io/stata.py
index f445f05c2ee05..3ce1680c109f9 100644
--- a/pandas/io/stata.py
+++ b/pandas/io/stata.py
@@ -627,7 +627,7 @@ def __init__(self, catarray: Series, encoding: str = "latin-1"):
 
     def generate_value_label(self, byteorder: str) -> bytes:
         """
-        Generate the binary representation of the value labals.
+        Generate the binary representation of the value labels.
 
         Parameters
         ----------
diff --git a/pandas/plotting/_core.py b/pandas/plotting/_core.py
index 748b83aec88f4..467bdf7e0745d 100644
--- a/pandas/plotting/_core.py
+++ b/pandas/plotting/_core.py
@@ -1694,7 +1694,7 @@ def _find_backend(backend: str):
     try:
         return _backends[backend]
     except KeyError:
-        # Fall back to unregisted, module name approach.
+        # Fall back to unregistered, module name approach.
         try:
             module = importlib.import_module(backend)
         except ImportError:
diff --git a/pandas/plotting/_matplotlib/core.py b/pandas/plotting/_matplotlib/core.py
index 46941e437a4ce..19a75eb151782 100644
--- a/pandas/plotting/_matplotlib/core.py
+++ b/pandas/plotting/_matplotlib/core.py
@@ -282,10 +282,10 @@ def _maybe_right_yaxis(self, ax, axes_num):
             return self._get_ax_layer(ax)
 
         if hasattr(ax, "right_ax"):
-            # if it has right_ax proparty, ``ax`` must be left axes
+            # if it has right_ax property, ``ax`` must be left axes
             return ax.right_ax
         elif hasattr(ax, "left_ax"):
-            # if it has left_ax proparty, ``ax`` must be right axes
+            # if it has left_ax property, ``ax`` must be right axes
             return ax
         else:
             # otherwise, create twin axes
@@ -387,7 +387,7 @@ def _compute_plot_data(self):
         if self.include_bool is True:
             include_type.append(np.bool_)
 
-        # GH22799, exclude datatime-like type for boxplot
+        # GH22799, exclude datetime-like type for boxplot
         exclude_type = None
         if self._kind == "box":
             # TODO: change after solving issue 27881
@@ -1103,7 +1103,7 @@ def _make_plot(self):
 
     @classmethod
     def _plot(cls, ax, x, y, style=None, column_num=None, stacking_id=None, **kwds):
-        # column_num is used to get the target column from protf in line and
+        # column_num is used to get the target column from plotf in line and
         # area plots
         if column_num == 0:
             cls._initialize_stacker(ax, stacking_id, len(y))
diff --git a/pandas/plotting/_matplotlib/misc.py b/pandas/plotting/_matplotlib/misc.py
index 7319e8de3ec6e..0cafcfed38a54 100644
--- a/pandas/plotting/_matplotlib/misc.py
+++ b/pandas/plotting/_matplotlib/misc.py
@@ -385,7 +385,7 @@ def parallel_coordinates(
 
 
 def lag_plot(series, lag=1, ax=None, **kwds):
-    # workaround because `c='b'` is hardcoded in matplotlibs scatter method
+    # workaround because `c='b'` is hardcoded in matplotlib's scatter method
     import matplotlib.pyplot as plt
 
     kwds.setdefault("c", plt.rcParams["patch.facecolor"])
diff --git a/pandas/plotting/_matplotlib/tools.py b/pandas/plotting/_matplotlib/tools.py
index 08d945f679810..ef8376bfef8a9 100644
--- a/pandas/plotting/_matplotlib/tools.py
+++ b/pandas/plotting/_matplotlib/tools.py
@@ -277,7 +277,7 @@ def _remove_labels_from_axis(axis):
         t.set_visible(False)
 
     # set_visible will not be effective if
-    # minor axis has NullLocator and NullFormattor (default)
+    # minor axis has NullLocator and NullFormatter (default)
     if isinstance(axis.get_minor_locator(), ticker.NullLocator):
         axis.set_minor_locator(ticker.AutoLocator())
     if isinstance(axis.get_minor_formatter(), ticker.NullFormatter):
diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py
index e1de9d1bcf832..aabc10609b78b 100644
--- a/pandas/tseries/offsets.py
+++ b/pandas/tseries/offsets.py
@@ -269,7 +269,7 @@ def apply(self, other):
     def apply_index(self, i):
         """
         Vectorized apply of DateOffset to DatetimeIndex,
-        raises NotImplentedError for offsets without a
+        raises NotImplementedError for offsets without a
         vectorized implementation.
 
         Parameters
@@ -667,7 +667,7 @@ def _get_business_hours_by_sec(self, start, end):
         """
         Return business hours in a day by seconds.
         """
-        # create dummy datetime to calculate businesshours in a day
+        # create dummy datetime to calculate business hours in a day
         dtstart = datetime(2014, 4, 1, start.hour, start.minute)
         day = 1 if start < end else 2
         until = datetime(2014, 4, day, end.hour, end.minute)
@@ -2216,7 +2216,7 @@ def _rollback_to_year(self, other):
             # roll adjustment
             qtr_lens = self.get_weeks(norm)
 
-            # check thet qtr_lens is consistent with self._offset addition
+            # check that qtr_lens is consistent with self._offset addition
             end = liboffsets.shift_day(start, days=7 * sum(qtr_lens))
             assert self._offset.is_on_offset(end), (start, end, qtr_lens)
 
diff --git a/pandas/util/_decorators.py b/pandas/util/_decorators.py
index b7bdbde5bac5e..26b5df862a07e 100644
--- a/pandas/util/_decorators.py
+++ b/pandas/util/_decorators.py
@@ -345,7 +345,7 @@ def doc(*args: Union[str, Callable], **kwargs: str) -> Callable[[F], F]:
     *args : str or callable
         The string / docstring / docstring template to be appended in order
         after default docstring under function.
-    **kwags : str
+    **kwargs : str
         The string which would be used to format docstring template.
     """
 
diff --git a/pandas/util/_doctools.py b/pandas/util/_doctools.py
index 71965b8e7dd9d..f413490764124 100644
--- a/pandas/util/_doctools.py
+++ b/pandas/util/_doctools.py
@@ -23,7 +23,7 @@ def __init__(
 
     def _shape(self, df: pd.DataFrame) -> Tuple[int, int]:
         """
-        Calculate table chape considering index levels.
+        Calculate table shape considering index levels.
         """
         row, col = df.shape
         return row + df.columns.nlevels, col + df.index.nlevels
diff --git a/pandas/util/_validators.py b/pandas/util/_validators.py
index 53a25eb321b73..fbb44408f01be 100644
--- a/pandas/util/_validators.py
+++ b/pandas/util/_validators.py
@@ -353,7 +353,7 @@ def validate_percentile(q: Union[float, Iterable[float]]) -> np.ndarray:
     """
     Validate percentiles (used by describe and quantile).
 
-    This function checks if the given float oriterable of floats is a valid percentile
+    This function checks if the given float or iterable of floats is a valid percentile
     otherwise raises a ValueError.
 
     Parameters