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)

pandas/core/arrays/sparse/accessor.py

@@ -33,7 +33,7 @@ class SparseAccessor(BaseAccessor, PandasDelegate):
     Accessor for SparseSparse from other sparse matrix data types.
     """
 
-    def _validate(self, data):
+    def _validate(self, data) -> None:
         if not isinstance(data.dtype, SparseDtype):
             raise AttributeError(self._validation_msg)
 
@@ -222,7 +222,7 @@ class SparseFrameAccessor(BaseAccessor, PandasDelegate):
     .. versionadded:: 0.25.0
     """
 
-    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

@@ -605,7 +605,7 @@ def __array__(self, dtype: NpDtype | None = None) -> np.ndarray:
         out[self.sp_index.indices] = self.sp_values
         return out
 
-    def __setitem__(self, key, value):
+    def __setitem__(self, key, value) -> None:
         # I suppose we could allow setting of non-fill_value elements.
         # TODO(SparseArray.__setitem__): remove special cases in
         # ExtensionBlock.where
@@ -657,7 +657,7 @@ def fill_value(self):
         return self.dtype.fill_value
 
     @fill_value.setter
-    def fill_value(self, value):
+    def fill_value(self, value) -> None:
         self._dtype = SparseDtype(self.dtype.subtype, value)
 
     @property
@@ -1381,7 +1381,7 @@ def _where(self, mask, value):
     # ------------------------------------------------------------------------
     # IO
     # ------------------------------------------------------------------------
-    def __setstate__(self, state):
+    def __setstate__(self, state) -> None:
         """Necessary for making this object picklable"""
         if isinstance(state, tuple):
             # Compat for pandas < 0.24.0
@@ -1795,7 +1795,7 @@ 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=False):
+    def _formatter(self, boxed=False) -> None:
         # 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/dtype.py

@@ -148,7 +148,7 @@ def fill_value(self):
         """
         return self._fill_value
 
-    def _check_fill_value(self):
+    def _check_fill_value(self) -> None:
         if not is_scalar(self._fill_value):
             raise ValueError(
                 f"fill_value must be a scalar. Got {self._fill_value} instead"

pandas/core/arrays/sparse/scipy_sparse.py

@@ -28,7 +28,7 @@
     import scipy.sparse
 
 
-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

@@ -304,7 +304,7 @@ def __init__(self, values, copy=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")
@@ -379,7 +379,7 @@ def _values_for_factorize(self):
         arr[mask] = -1
         return arr, -1
 
-    def __setitem__(self, key, value):
+    def __setitem__(self, key, value) -> None:
         value = extract_array(value, extract_numpy=True)
         if isinstance(value, type(self)):
             # extract_array doesn't extract PandasArray subclasses

pandas/core/arrays/timedeltas.py

@@ -439,7 +439,7 @@ def _format_native_types(
     # ----------------------------------------------------------------
     # Arithmetic Methods
 
-    def _add_offset(self, other):
+    def _add_offset(self, other) -> None:
         assert not isinstance(other, Tick)
         raise TypeError(
             f"cannot add the type {type(other).__name__} to a {type(self).__name__}"

pandas/core/base.py

@@ -154,14 +154,14 @@ class NoNewAttributesMixin:
     `object.__setattr__(self, key, value)`.
     """
 
-    def _freeze(self):
+    def _freeze(self) -> None:
         """
         Prevents setting additional attributes.
         """
         object.__setattr__(self, "__frozen", True)
 
     # prevent adding any attribute via s.xxx.new_attribute = ...
-    def __setattr__(self, key: str, value):
+    def __setattr__(self, key: str, value) -> None:
         # _cache is used by a decorator
         # We need to check both 1.) cls.__dict__ and 2.) getattr(self, key)
         # because

pandas/core/common.py

@@ -553,7 +553,7 @@ def temp_setattr(obj, attr: str, value) -> Iterator[None]:
         setattr(obj, attr, old_value)
 
 
-def require_length_match(data, index: Index):
+def require_length_match(data, index: Index) -> None:
     """
     Check the length of data matches the length of the index.
     """
@@ -665,7 +665,9 @@ def resolve_numeric_only(numeric_only: bool | None | lib.NoDefault) -> bool:
     return result
 
 
-def deprecate_numeric_only_default(cls: type, name: str, deprecate_none: bool = False):
+def deprecate_numeric_only_default(
+    cls: type, name: str, deprecate_none: bool = False
+) -> None:
     """Emit FutureWarning message for deprecation of numeric_only.
 
     See GH#46560 for details on the deprecation.

pandas/core/computation/eval.py

@@ -67,7 +67,7 @@ def _check_engine(engine: str | None) -> str:
     return engine
 
 
-def _check_parser(parser: str):
+def _check_parser(parser: str) -> None:
     """
     Make sure a valid parser is passed.
 
@@ -86,7 +86,7 @@ def _check_parser(parser: str):
         )
 
 
-def _check_resolvers(resolvers):
+def _check_resolvers(resolvers) -> None:
     if resolvers is not None:
         for resolver in resolvers:
             if not hasattr(resolver, "__getitem__"):
@@ -97,7 +97,7 @@ def _check_resolvers(resolvers):
                 )
 
 
-def _check_expression(expr):
+def _check_expression(expr) -> None:
     """
     Make sure an expression is not an empty string
 
@@ -144,7 +144,7 @@ def _convert_expression(expr) -> str:
     return s
 
 
-def _check_for_locals(expr: str, stack_level: int, parser: str):
+def _check_for_locals(expr: str, stack_level: int, parser: str) -> None:
 
     at_top_of_stack = stack_level == 0
     not_pandas_parser = parser != "pandas"

pandas/core/computation/expressions.py

@@ -38,7 +38,7 @@
 _MIN_ELEMENTS = 1_000_000
 
 
-def set_use_numexpr(v=True):
+def set_use_numexpr(v=True) -> None:
     # set/unset to use numexpr
     global USE_NUMEXPR
     if NUMEXPR_INSTALLED:
@@ -51,7 +51,7 @@ def set_use_numexpr(v=True):
     _where = _where_numexpr if USE_NUMEXPR else _where_standard
 
 
-def set_numexpr_threads(n=None):
+def set_numexpr_threads(n=None) -> None:
     # if we are using numexpr, set the threads to n
     # otherwise reset
     if NUMEXPR_INSTALLED and USE_NUMEXPR: