CLN: changes for spelling mistakes

pandas/core/dtypes/astype.py

@@ -181,7 +181,7 @@ def astype_array(values: ArrayLike, dtype: DtypeObj, copy: bool = False) -> Arra
     else:
         values = _astype_nansafe(values, dtype, copy=copy)
 
-    # in pandas we don't store numpy str dtypes, so convert to object
+    # in pandas, we don't store numpy str dtypes, so convert to object
     if isinstance(dtype, np.dtype) and issubclass(values.dtype.type, str):
         values = np.array(values, dtype=object)
 

pandas/core/dtypes/base.py

@@ -119,7 +119,7 @@ def __eq__(self, other: object) -> bool:
         By default, 'other' is considered equal if either
 
         * it's a string matching 'self.name'.
-        * it's an instance of this type and all of the attributes
+        * it's an instance of this type and all the attributes
           in ``self._metadata`` are equal between `self` and `other`.
 
         Parameters
@@ -339,7 +339,7 @@ def _is_numeric(self) -> bool:
         """
         Whether columns with this dtype should be considered numeric.
 
-        By default ExtensionDtypes are assumed to be non-numeric.
+        By default, ExtensionDtypes are assumed to be non-numeric.
         They'll be excluded from operations that exclude non-numeric
         columns, like (groupby) reductions, plotting, etc.
         """

pandas/core/dtypes/cast.py

@@ -454,7 +454,7 @@ def maybe_cast_pointwise_result(
     dtype : np.dtype or ExtensionDtype
         Input Series from which result was calculated.
     numeric_only : bool, default False
-        Whether to cast only numerics or datetimes as well.
+        Whether to cast only numerics or datetime as well.
     same_dtype : bool, default True
         Specify dtype when calling _from_sequence
 

pandas/core/dtypes/common.py

@@ -1120,7 +1120,7 @@ def is_any_real_numeric_dtype(arr_or_dtype) -> bool:
     Returns
     -------
     boolean
-        Whether or not the array or dtype is of a real number dtype.
+        Whether the array or dtype is of a real number dtype.
 
     Examples
     --------

pandas/core/dtypes/concat.py

@@ -129,7 +129,7 @@ def concat_compat(
         # i.e. isinstance(to_concat[0], ExtensionArray)
         to_concat_eas = cast("Sequence[ExtensionArray]", to_concat)
         cls = type(to_concat[0])
-        # GH#53640: eg. for datetime array, axis=1 but 0 is default
+        # GH#53640: e.g. for datetime array, axis=1 but 0 is default
         # However, class method `_concat_same_type()` for some classes
         # may not support the `axis` keyword
         if ea_compat_axis or axis == 0:
@@ -337,7 +337,7 @@ def _maybe_unwrap(x):
     else:
         # ordered - to show a proper error message
         if all(c.ordered for c in to_union):
-            msg = "to union ordered Categoricals, all categories must be the same"
+            msg = "to union ordered Categorical, all categories must be the same"
             raise TypeError(msg)
         raise TypeError("Categorical.ordered must be the same")
 

pandas/core/groupby/categorical.py

@@ -14,7 +14,7 @@ def recode_for_groupby(
     c: Categorical, sort: bool, observed: bool
 ) -> tuple[Categorical, Categorical | None]:
     """
-    Code the categories to ensure we can groupby for categoricals.
+    Code the categories to ensure we can groupby for categorical.
 
     If observed=True, we return a new Categorical with the observed
     categories only.

pandas/core/groupby/grouper.py

@@ -1051,7 +1051,7 @@ def is_in_obj(gpr) -> bool:
             in_axis = False
 
         # create the Grouping
-        # allow us to passing the actual Grouping as the gpr
+        # allow us to pass the actual Grouping as the gpr
         ping = (
             Grouping(
                 group_axis,

pandas/plotting/_core.py

@@ -992,7 +992,7 @@ def __call__(self, *args, **kwargs):
                 if y is not None:
                     if is_integer(y) and not data.columns._holds_integer():
                         y = data.columns[y]
-                    # converted to series actually. copy to not modify
+                    # converted to series, actually. copy to not modify
                     data = data[y].copy()
                     data.index.name = y
         elif isinstance(data, ABCDataFrame):

scripts/validate_unwanted_patterns.py

@@ -202,7 +202,7 @@ def private_function_across_module(file_obj: IO[str]) -> Iterable[tuple[int, str
             continue
 
         if module_name in imported_modules and function_name.startswith("_"):
-            yield (node.lineno, f"Private function '{module_name}.{function_name}'")
+            yield node.lineno, f"Private function '{module_name}.{function_name}'"
 
 
 def private_import_across_module(file_obj: IO[str]) -> Iterable[tuple[int, str]]:
@@ -232,7 +232,7 @@ def private_import_across_module(file_obj: IO[str]) -> Iterable[tuple[int, str]]
                 continue
 
             if module_name.startswith("_"):
-                yield (node.lineno, f"Import of internal function {repr(module_name)}")
+                yield node.lineno, f"Import of internal function {repr(module_name)}"
 
 
 def strings_with_wrong_placed_whitespace(