CLN: spelling fixes in docstrings (#34039)

* spelling fixes

* add whatsnew entry

* fix doctests

* and one more that wasn't caught

Author: akosfurton

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():

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]]
 

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

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")

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

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):

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()

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)

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.
 """
 
 

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