TYP: more simple return types from ruff (pandas-dev#56628)

TYP: more return types from ruff

Author: twoertwein

pandas/core/apply.py

@@ -827,7 +827,7 @@ def generate_numba_apply_func(
     def apply_with_numba(self):
         pass
 
-    def validate_values_for_numba(self):
+    def validate_values_for_numba(self) -> None:
         # Validate column dtyps all OK
         for colname, dtype in self.obj.dtypes.items():
             if not is_numeric_dtype(dtype):

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/arrays/arrow/accessors.py

@@ -39,7 +39,7 @@ def __init__(self, data, validation_msg: str) -> None:
     def _is_valid_pyarrow_dtype(self, pyarrow_dtype) -> bool:
         pass
 
-    def _validate(self, data):
+    def _validate(self, data) -> None:
         dtype = data.dtype
         if not isinstance(dtype, ArrowDtype):
             # Raise AttributeError so that inspect can handle non-struct Series.

pandas/core/arrays/arrow/extension_types.py

@@ -135,7 +135,7 @@ def to_pandas_dtype(self) -> IntervalDtype:
 """
 
 
-def patch_pyarrow():
+def patch_pyarrow() -> None:
     # starting from pyarrow 14.0.1, it has its own mechanism
     if not pa_version_under14p1:
         return

pandas/core/arrays/categorical.py

@@ -2164,7 +2164,9 @@ def __contains__(self, key) -> bool:
     # ------------------------------------------------------------------
     # Rendering Methods
 
-    def _formatter(self, boxed: bool = False):
+    # error: Return type "None" of "_formatter" incompatible with return
+    # type "Callable[[Any], str | None]" in supertype "ExtensionArray"
+    def _formatter(self, boxed: bool = False) -> None:  # type: ignore[override]
         # Returning None here will cause format_array to do inference.
         return None
 
@@ -2890,7 +2892,7 @@ def __init__(self, data) -> None:
         self._freeze()
 
     @staticmethod
-    def _validate(data):
+    def _validate(data) -> None:
         if not isinstance(data.dtype, CategoricalDtype):
             raise AttributeError("Can only use .cat accessor with a 'category' dtype")
 

pandas/core/arrays/datetimelike.py

@@ -2058,7 +2058,7 @@ def freq(self, value) -> None:
         self._freq = value
 
     @final
-    def _maybe_pin_freq(self, freq, validate_kwds: dict):
+    def _maybe_pin_freq(self, freq, validate_kwds: dict) -> None:
         """
         Constructor helper to pin the appropriate `freq` attribute.  Assumes
         that self._freq is currently set to any freq inferred in
@@ -2092,7 +2092,7 @@ def _maybe_pin_freq(self, freq, validate_kwds: dict):
 
     @final
     @classmethod
-    def _validate_frequency(cls, index, freq: BaseOffset, **kwargs):
+    def _validate_frequency(cls, index, freq: BaseOffset, **kwargs) -> None:
         """
         Validate that a frequency is compatible with the values of a given
         Datetime Array/Index or Timedelta Array/Index

pandas/core/arrays/sparse/accessor.py

@@ -30,7 +30,7 @@ def __init__(self, data=None) -> None:
         self._parent = data
         self._validate(data)
 
-    def _validate(self, data):
+    def _validate(self, data) -> None:
         raise NotImplementedError
 
 
@@ -50,7 +50,7 @@ class SparseAccessor(BaseAccessor, PandasDelegate):
     array([2, 2, 2])
     """
 
-    def _validate(self, data):
+    def _validate(self, data) -> None:
         if not isinstance(data.dtype, SparseDtype):
             raise AttributeError(self._validation_msg)
 
@@ -243,7 +243,7 @@ class SparseFrameAccessor(BaseAccessor, PandasDelegate):
     0.5
     """
 
-    def _validate(self, data):
+    def _validate(self, data) -> None:
         dtypes = data.dtypes
         if not all(isinstance(t, SparseDtype) for t in dtypes):
             raise AttributeError(self._validation_msg)

pandas/core/arrays/sparse/array.py

@@ -1830,7 +1830,9 @@ def __repr__(self) -> str:
         pp_index = printing.pprint_thing(self.sp_index)
         return f"{pp_str}\nFill: {pp_fill}\n{pp_index}"
 
-    def _formatter(self, boxed: bool = False):
+    # error: Return type "None" of "_formatter" incompatible with return
+    # type "Callable[[Any], str | None]" in supertype "ExtensionArray"
+    def _formatter(self, boxed: bool = False) -> None:  # type: ignore[override]
         # Defer to the formatter from the GenericArrayFormatter calling us.
         # This will infer the correct formatter from the dtype of the values.
         return None

pandas/core/arrays/sparse/scipy_sparse.py

@@ -27,7 +27,7 @@
     )
 
 
-def _check_is_partition(parts: Iterable, whole: Iterable):
+def _check_is_partition(parts: Iterable, whole: Iterable) -> None:
     whole = set(whole)
     parts = [set(x) for x in parts]
     if set.intersection(*parts) != set():

pandas/core/arrays/string_.py

@@ -364,7 +364,7 @@ def __init__(self, values, copy: bool = False) -> None:
             self._validate()
         NDArrayBacked.__init__(self, self._ndarray, StringDtype(storage="python"))
 
-    def _validate(self):
+    def _validate(self) -> None:
         """Validate that we only store NA or strings."""
         if len(self._ndarray) and not lib.is_string_array(self._ndarray, skipna=True):
             raise ValueError("StringArray requires a sequence of strings or pandas.NA")