TYP: mostly Hashtable and ArrowExtensionArray (pandas-dev#56689)

* TYP: mostly Hashtable and ArrowExtensionArray

* fix mypy stubtest

* and return types for core.arrays

* pyupgrade

* runtime actually expectes np.bool_ (calls .reshape(1) on it)

* TypeVar

* return bool | NAType

* isort

pandas/_libs/hashtable.pyi

@@ -2,6 +2,7 @@ from typing import (
     Any,
     Hashable,
     Literal,
+    overload,
 )
 
 import numpy as np
@@ -180,18 +181,30 @@ class HashTable:
         na_value: object = ...,
         mask=...,
     ) -> npt.NDArray[np.intp]: ...
+    @overload
     def unique(
         self,
         values: np.ndarray,  # np.ndarray[subclass-specific]
-        return_inverse: bool = ...,
-        mask=...,
-    ) -> (
-        tuple[
-            np.ndarray,  # np.ndarray[subclass-specific]
-            npt.NDArray[np.intp],
-        ]
-        | np.ndarray
-    ): ...  # np.ndarray[subclass-specific]
+        *,
+        return_inverse: Literal[False] = ...,
+        mask: None = ...,
+    ) -> np.ndarray: ...  # np.ndarray[subclass-specific]
+    @overload
+    def unique(
+        self,
+        values: np.ndarray,  # np.ndarray[subclass-specific]
+        *,
+        return_inverse: Literal[True],
+        mask: None = ...,
+    ) -> tuple[np.ndarray, npt.NDArray[np.intp],]: ...  # np.ndarray[subclass-specific]
+    @overload
+    def unique(
+        self,
+        values: np.ndarray,  # np.ndarray[subclass-specific]
+        *,
+        return_inverse: Literal[False] = ...,
+        mask: npt.NDArray[np.bool_],
+    ) -> tuple[np.ndarray, npt.NDArray[np.bool_],]: ...  # np.ndarray[subclass-specific]
     def factorize(
         self,
         values: np.ndarray,  # np.ndarray[subclass-specific]

pandas/_libs/hashtable_class_helper.pxi.in

@@ -755,7 +755,7 @@ cdef class {{name}}HashTable(HashTable):
             return uniques.to_array(), result_mask.to_array()
         return uniques.to_array()
 
-    def unique(self, const {{dtype}}_t[:] values, bint return_inverse=False, object mask=None):
+    def unique(self, const {{dtype}}_t[:] values, *, bint return_inverse=False, object mask=None):
         """
         Calculate unique values and labels (no sorting!)
 
@@ -1180,7 +1180,7 @@ cdef class StringHashTable(HashTable):
             return uniques.to_array(), labels.base  # .base -> underlying ndarray
         return uniques.to_array()
 
-    def unique(self, ndarray[object] values, bint return_inverse=False, object mask=None):
+    def unique(self, ndarray[object] values, *, bint return_inverse=False, object mask=None):
         """
         Calculate unique values and labels (no sorting!)
 
@@ -1438,7 +1438,7 @@ cdef class PyObjectHashTable(HashTable):
             return uniques.to_array(), labels.base  # .base -> underlying ndarray
         return uniques.to_array()
 
-    def unique(self, ndarray[object] values, bint return_inverse=False, object mask=None):
+    def unique(self, ndarray[object] values, *, bint return_inverse=False, object mask=None):
         """
         Calculate unique values and labels (no sorting!)
 

pandas/_typing.py

@@ -109,6 +109,7 @@
 # array-like
 
 ArrayLike = Union["ExtensionArray", np.ndarray]
+ArrayLikeT = TypeVar("ArrayLikeT", "ExtensionArray", np.ndarray)
 AnyArrayLike = Union[ArrayLike, "Index", "Series"]
 TimeArrayLike = Union["DatetimeArray", "TimedeltaArray"]
 
@@ -137,7 +138,7 @@ def __len__(self) -> int:
     def __iter__(self) -> Iterator[_T_co]:
         ...
 
-    def index(self, value: Any, /, start: int = 0, stop: int = ...) -> int:
+    def index(self, value: Any, start: int = ..., stop: int = ..., /) -> int:
         ...
 
     def count(self, value: Any, /) -> int:

pandas/compat/pickle_compat.py

@@ -7,7 +7,10 @@
 import copy
 import io
 import pickle as pkl
-from typing import TYPE_CHECKING
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
 
 import numpy as np
 
@@ -209,7 +212,7 @@ def load_newobj_ex(self) -> None:
     pass
 
 
-def load(fh, encoding: str | None = None, is_verbose: bool = False):
+def load(fh, encoding: str | None = None, is_verbose: bool = False) -> Any:
     """
     Load a pickle, with a provided encoding,
 
@@ -239,7 +242,7 @@ def loads(
     fix_imports: bool = True,
     encoding: str = "ASCII",
     errors: str = "strict",
-):
+) -> Any:
     """
     Analogous to pickle._loads.
     """

pandas/core/accessor.py

@@ -54,7 +54,7 @@ class PandasDelegate:
     def _delegate_property_get(self, name: str, *args, **kwargs):
         raise TypeError(f"You cannot access the property {name}")
 
-    def _delegate_property_set(self, name: str, value, *args, **kwargs):
+    def _delegate_property_set(self, name: str, value, *args, **kwargs) -> None:
         raise TypeError(f"The property {name} cannot be set")
 
     def _delegate_method(self, name: str, *args, **kwargs):

pandas/core/algorithms.py

@@ -25,6 +25,7 @@
 from pandas._typing import (
     AnyArrayLike,
     ArrayLike,
+    ArrayLikeT,
     AxisInt,
     DtypeObj,
     TakeIndexer,
@@ -182,8 +183,8 @@ def _ensure_data(values: ArrayLike) -> np.ndarray:
 
 
 def _reconstruct_data(
-    values: ArrayLike, dtype: DtypeObj, original: AnyArrayLike
-) -> ArrayLike:
+    values: ArrayLikeT, dtype: DtypeObj, original: AnyArrayLike
+) -> ArrayLikeT:
     """
     reverse of _ensure_data
 
@@ -206,7 +207,9 @@ def _reconstruct_data(
         #  that values.dtype == dtype
         cls = dtype.construct_array_type()
 
-        values = cls._from_sequence(values, dtype=dtype)
+        # error: Incompatible types in assignment (expression has type
+        # "ExtensionArray", variable has type "ndarray[Any, Any]")
+        values = cls._from_sequence(values, dtype=dtype)  # type: ignore[assignment]
 
     else:
         values = values.astype(dtype, copy=False)
@@ -259,7 +262,9 @@ def _ensure_arraylike(values, func_name: str) -> ArrayLike:
 }
 
 
-def _get_hashtable_algo(values: np.ndarray):
+def _get_hashtable_algo(
+    values: np.ndarray,
+) -> tuple[type[htable.HashTable], np.ndarray]:
     """
     Parameters
     ----------
@@ -1550,7 +1555,9 @@ def safe_sort(
         hash_klass, values = _get_hashtable_algo(values)  # type: ignore[arg-type]
         t = hash_klass(len(values))
         t.map_locations(values)
-        sorter = ensure_platform_int(t.lookup(ordered))
+        # error: Argument 1 to "lookup" of "HashTable" has incompatible type
+        # "ExtensionArray | ndarray[Any, Any] | Index | Series"; expected "ndarray"
+        sorter = ensure_platform_int(t.lookup(ordered))  # type: ignore[arg-type]
 
     if use_na_sentinel:
         # take_nd is faster, but only works for na_sentinels of -1