add some -> to pandas core

Author: MarcoGorelli

pandas/core/array_algos/replace.py

@@ -67,7 +67,7 @@ def compare_or_regex_search(
 
     def _check_comparison_types(
         result: ArrayLike | bool, a: ArrayLike, b: Scalar | Pattern
-    ):
+    ) -> None:
         """
         Raises an error if the two arrays (a,b) cannot be compared.
         Otherwise, returns the comparison result as expected.

pandas/core/array_algos/take.py

@@ -337,7 +337,7 @@ def _get_take_nd_function(
 
     if func is None:
 
-        def func(arr, indexer, out, fill_value=np.nan):
+        def func(arr, indexer, out, fill_value=np.nan) -> None:
             indexer = ensure_platform_int(indexer)
             _take_nd_object(
                 arr, indexer, out, axis=axis, fill_value=fill_value, mask_info=mask_info
@@ -349,7 +349,7 @@ def func(arr, indexer, out, fill_value=np.nan):
 def _view_wrapper(f, arr_dtype=None, out_dtype=None, fill_wrap=None):
     def wrapper(
         arr: np.ndarray, indexer: np.ndarray, out: np.ndarray, fill_value=np.nan
-    ):
+    ) -> None:
         if arr_dtype is not None:
             arr = arr.view(arr_dtype)
         if out_dtype is not None:
@@ -364,7 +364,7 @@ def wrapper(
 def _convert_wrapper(f, conv_dtype):
     def wrapper(
         arr: np.ndarray, indexer: np.ndarray, out: np.ndarray, fill_value=np.nan
-    ):
+    ) -> None:
         if conv_dtype == object:
             # GH#39755 avoid casting dt64/td64 to integers
             arr = ensure_wrapped_if_datetimelike(arr)
@@ -506,7 +506,7 @@ def _take_nd_object(
     axis: int,
     fill_value,
     mask_info,
-):
+) -> None:
     if mask_info is not None:
         mask, needs_masking = mask_info
     else:

pandas/core/arrays/_mixins.py

@@ -259,7 +259,7 @@ def _validate_shift_value(self, fill_value):
         #  we can remove this and use validate_fill_value directly
         return self._validate_scalar(fill_value)
 
-    def __setitem__(self, key, value):
+    def __setitem__(self, key, value) -> None:
         key = check_array_indexer(self, key)
         value = self._validate_setitem_value(value)
         self._ndarray[key] = value

pandas/core/arrays/arrow/_arrow_utils.py

@@ -14,7 +14,7 @@
 from pandas.core.arrays.interval import VALID_CLOSED
 
 
-def fallback_performancewarning(version: str | None = None):
+def fallback_performancewarning(version: str | None = None) -> None:
     """
     Raise a PerformanceWarning for falling back to ExtensionArray's
     non-pyarrow method

pandas/core/arrays/base.py

@@ -1656,7 +1656,7 @@ def _create_arithmetic_method(cls, op):
         raise AbstractMethodError(cls)
 
     @classmethod
-    def _add_arithmetic_ops(cls):
+    def _add_arithmetic_ops(cls) -> None:
         setattr(cls, "__add__", cls._create_arithmetic_method(operator.add))
         setattr(cls, "__radd__", cls._create_arithmetic_method(roperator.radd))
         setattr(cls, "__sub__", cls._create_arithmetic_method(operator.sub))
@@ -1681,7 +1681,7 @@ def _create_comparison_method(cls, op):
         raise AbstractMethodError(cls)
 
     @classmethod
-    def _add_comparison_ops(cls):
+    def _add_comparison_ops(cls) -> None:
         setattr(cls, "__eq__", cls._create_comparison_method(operator.eq))
         setattr(cls, "__ne__", cls._create_comparison_method(operator.ne))
         setattr(cls, "__lt__", cls._create_comparison_method(operator.lt))
@@ -1694,7 +1694,7 @@ def _create_logical_method(cls, op):
         raise AbstractMethodError(cls)
 
     @classmethod
-    def _add_logical_ops(cls):
+    def _add_logical_ops(cls) -> None:
         setattr(cls, "__and__", cls._create_logical_method(operator.and_))
         setattr(cls, "__rand__", cls._create_logical_method(roperator.rand_))
         setattr(cls, "__or__", cls._create_logical_method(operator.or_))

pandas/core/arrays/categorical.py

@@ -738,7 +738,7 @@ def categories(self):
         return self.dtype.categories
 
     @categories.setter
-    def categories(self, categories):
+    def categories(self, categories) -> None:
         new_dtype = CategoricalDtype(categories, ordered=self.ordered)
         if self.dtype.categories is not None and len(self.dtype.categories) != len(
             new_dtype.categories
@@ -776,7 +776,7 @@ def codes(self) -> np.ndarray:
         v.flags.writeable = False
         return v
 
-    def _set_categories(self, categories, fastpath=False):
+    def _set_categories(self, categories, fastpath=False) -> None:
         """
         Sets new categories inplace
 
@@ -1700,7 +1700,7 @@ def _internal_get_values(self):
             return self.categories.astype("object").take(self._codes, fill_value=np.nan)
         return np.array(self)
 
-    def check_for_ordered(self, op):
+    def check_for_ordered(self, op) -> None:
         """assert that we are ordered"""
         if not self.ordered:
             raise TypeError(
@@ -1931,7 +1931,7 @@ def _codes(self) -> np.ndarray:
         return self._ndarray
 
     @_codes.setter
-    def _codes(self, value: np.ndarray):
+    def _codes(self, value: np.ndarray) -> None:
         warn(
             "Setting the codes on a Categorical is deprecated and will raise in "
             "a future version. Create a new Categorical object instead",
@@ -1985,7 +1985,7 @@ def __contains__(self, key) -> bool:
     # ------------------------------------------------------------------
     # Rendering Methods
 
-    def _formatter(self, boxed: bool = False):
+    def _formatter(self, boxed: bool = False) -> None:
         # Defer to CategoricalFormatter's formatter.
         return None
 
@@ -2713,7 +2713,7 @@ def __init__(self, data) -> None:
         self._freeze()
 
     @staticmethod
-    def _validate(data):
+    def _validate(data) -> None:
         if not is_categorical_dtype(data.dtype):
             raise AttributeError("Can only use .cat accessor with a 'category' dtype")
 

pandas/core/arrays/datetimelike.py

@@ -410,7 +410,7 @@ def __setitem__(  # type: ignore[override]
 
         self._maybe_clear_freq()
 
-    def _maybe_clear_freq(self):
+    def _maybe_clear_freq(self) -> None:
         # inplace operations like __setitem__ may invalidate the freq of
         # DatetimeArray and TimedeltaArray
         pass
@@ -924,7 +924,7 @@ def freq(self):
         return self._freq
 
     @freq.setter
-    def freq(self, value):
+    def freq(self, value) -> None:
         if value is not None:
             value = to_offset(value)
             self._validate_frequency(self, value)
@@ -976,7 +976,7 @@ def resolution(self) -> str:
         return self._resolution_obj.attrname  # type: ignore[union-attr]
 
     @classmethod
-    def _validate_frequency(cls, index, freq, **kwargs):
+    def _validate_frequency(cls, index, freq, **kwargs) -> None:
         """
         Validate that a frequency is compatible with the values of a given
         Datetime Array/Index or Timedelta Array/Index

pandas/core/arrays/datetimes.py

@@ -520,7 +520,7 @@ def _unbox_scalar(self, value, setitem: bool = False) -> np.datetime64:
     def _scalar_from_string(self, value) -> Timestamp | NaTType:
         return Timestamp(value, tz=self.tz)
 
-    def _check_compatible_with(self, other, setitem: bool = False):
+    def _check_compatible_with(self, other, setitem: bool = False) -> None:
         if other is NaT:
             return
         self._assert_tzawareness_compat(other)

pandas/core/arrays/interval.py

@@ -598,7 +598,7 @@ def from_tuples(
 
         return cls.from_arrays(left, right, inclusive, copy=False, dtype=dtype)
 
-    def _validate(self):
+    def _validate(self) -> None:
         """
         Verify that the IntervalArray is valid.
 
@@ -692,7 +692,7 @@ def __getitem__(
             raise ValueError("multi-dimensional indexing not allowed")
         return self._shallow_copy(left, right)
 
-    def __setitem__(self, key, value):
+    def __setitem__(self, key, value) -> None:
         value_left, value_right = self._validate_setitem_value(value)
         key = check_array_indexer(self, key)
 

pandas/core/arrays/period.py

@@ -336,7 +336,7 @@ def _unbox_scalar(  # type: ignore[override]
     def _scalar_from_string(self, value: str) -> Period:
         return Period(value, freq=self.freq)
 
-    def _check_compatible_with(self, other, setitem: bool = False):
+    def _check_compatible_with(self, other, setitem: bool = False) -> None:
         if other is NaT:
             return
         self._require_matching_freq(other)