TYP: more return annotations in core/ (pandas-dev#47618)

* TYP: more return annotations in core/

* from __future__ import annotations

* more __future__

Author: twoertwein

pandas/_testing/__init__.py

@@ -529,7 +529,7 @@ def getMixedTypeDict():
     return index, data
 
 
-def makeMixedDataFrame():
+def makeMixedDataFrame() -> DataFrame:
     return DataFrame(getMixedTypeDict()[1])
 
 

pandas/core/array_algos/replace.py

@@ -119,7 +119,7 @@ def _check_comparison_types(
 
 def replace_regex(
     values: ArrayLike, rx: re.Pattern, value, mask: npt.NDArray[np.bool_] | None
-):
+) -> None:
     """
     Parameters
     ----------

pandas/core/arrays/_mixins.py

@@ -70,6 +70,8 @@
         NumpyValueArrayLike,
     )
 
+    from pandas import Series
+
 
 def ravel_compat(meth: F) -> F:
     """
@@ -259,7 +261,7 @@ 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
@@ -433,7 +435,7 @@ def insert(
     #  These are not part of the EA API, but we implement them because
     #  pandas assumes they're there.
 
-    def value_counts(self, dropna: bool = True):
+    def value_counts(self, dropna: bool = True) -> Series:
         """
         Return a Series containing counts of unique values.
 

pandas/core/arrays/arrow/_arrow_utils.py

@@ -13,7 +13,7 @@
 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
@@ -24,7 +24,9 @@ def fallback_performancewarning(version: str | None = None):
     warnings.warn(msg, PerformanceWarning, stacklevel=find_stack_level())
 
 
-def pyarrow_array_to_numpy_and_mask(arr, dtype: np.dtype):
+def pyarrow_array_to_numpy_and_mask(
+    arr, dtype: np.dtype
+) -> tuple[np.ndarray, np.ndarray]:
     """
     Convert a primitive pyarrow.Array to a numpy array and boolean mask based
     on the buffers of the Array.
@@ -74,12 +76,12 @@ def __init__(self, freq) -> None:
     def freq(self):
         return self._freq
 
-    def __arrow_ext_serialize__(self):
+    def __arrow_ext_serialize__(self) -> bytes:
         metadata = {"freq": self.freq}
         return json.dumps(metadata).encode()
 
     @classmethod
-    def __arrow_ext_deserialize__(cls, storage_type, serialized):
+    def __arrow_ext_deserialize__(cls, storage_type, serialized) -> ArrowPeriodType:
         metadata = json.loads(serialized.decode())
         return ArrowPeriodType(metadata["freq"])
 
@@ -122,7 +124,7 @@ def subtype(self):
         return self._subtype
 
     @property
-    def inclusive(self):
+    def inclusive(self) -> str:
         return self._closed
 
     @property
@@ -134,12 +136,12 @@ def closed(self):
         )
         return self._closed
 
-    def __arrow_ext_serialize__(self):
+    def __arrow_ext_serialize__(self) -> bytes:
         metadata = {"subtype": str(self.subtype), "inclusive": self.inclusive}
         return json.dumps(metadata).encode()
 
     @classmethod
-    def __arrow_ext_deserialize__(cls, storage_type, serialized):
+    def __arrow_ext_deserialize__(cls, storage_type, serialized) -> ArrowIntervalType:
         metadata = json.loads(serialized.decode())
         subtype = pyarrow.type_for_alias(metadata["subtype"])
         inclusive = metadata["inclusive"]

pandas/core/arrays/arrow/array.py

@@ -368,7 +368,7 @@ def take(
         indices: TakeIndexer,
         allow_fill: bool = False,
         fill_value: Any = None,
-    ):
+    ) -> ArrowExtensionArray:
         """
         Take elements from an array.
 

pandas/core/arrays/arrow/dtype.py

@@ -77,7 +77,7 @@ def construct_array_type(cls):
         return ArrowExtensionArray
 
     @classmethod
-    def construct_from_string(cls, string: str):
+    def construct_from_string(cls, string: str) -> ArrowDtype:
         """
         Construct this type from a string.
 

pandas/core/arrays/base.py

@@ -460,7 +460,7 @@ def __ne__(self, other: Any) -> ArrayLike:  # type: ignore[override]
         """
         return ~(self == other)
 
-    def __init_subclass__(cls, **kwargs):
+    def __init_subclass__(cls, **kwargs) -> None:
         factorize = getattr(cls, "factorize")
         if (
             "use_na_sentinel" not in inspect.signature(factorize).parameters
@@ -770,11 +770,11 @@ def argmax(self, skipna: bool = True) -> int:
         return nargminmax(self, "argmax")
 
     def fillna(
-        self,
+        self: ExtensionArrayT,
         value: object | ArrayLike | None = None,
         method: FillnaOptions | None = None,
         limit: int | None = None,
-    ):
+    ) -> ExtensionArrayT:
         """
         Fill NA/NaN values using the specified method.
 
@@ -1139,7 +1139,9 @@ def factorize(
 
     @Substitution(klass="ExtensionArray")
     @Appender(_extension_array_shared_docs["repeat"])
-    def repeat(self, repeats: int | Sequence[int], axis: int | None = None):
+    def repeat(
+        self: ExtensionArrayT, repeats: int | Sequence[int], axis: int | None = None
+    ) -> ExtensionArrayT:
         nv.validate_repeat((), {"axis": axis})
         ind = np.arange(len(self)).repeat(repeats)
         return self.take(ind)