BUG: incorrect is_array_like checks

pandas/_typing.py

@@ -155,7 +155,7 @@
 
 RandomState = Union[
     int,
-    ArrayLike,
+    np.ndarray,
     np.random.Generator,
     np.random.BitGenerator,
     np.random.RandomState,

pandas/core/arrays/arrow/array.py

@@ -18,22 +18,6 @@
 import numpy as np
 
 from pandas._libs import lib
-from pandas._typing import (
-    ArrayLike,
-    AxisInt,
-    Dtype,
-    FillnaOptions,
-    Iterator,
-    NpDtype,
-    PositionalIndexer,
-    Scalar,
-    Self,
-    SortKind,
-    TakeIndexer,
-    TimeAmbiguous,
-    TimeNonexistent,
-    npt,
-)
 from pandas.compat import (
     pa_version_under7p0,
     pa_version_under8p0,
@@ -140,8 +124,22 @@ def floordiv_compat(
 
 if TYPE_CHECKING:
     from pandas._typing import (
+        ArrayLike,
+        AxisInt,
+        Dtype,
+        FillnaOptions,
+        Iterator,
+        NpDtype,
         NumpySorter,
         NumpyValueArrayLike,
+        PositionalIndexer,
+        Scalar,
+        Self,
+        SortKind,
+        TakeIndexer,
+        TimeAmbiguous,
+        TimeNonexistent,
+        npt,
     )
 
     from pandas import Series
@@ -805,8 +803,9 @@ def fillna(
             fallback_performancewarning()
             return super().fillna(value=value, method=method, limit=limit)
 
-        if is_array_like(value):
-            value = cast(ArrayLike, value)
+        if isinstance(value, (np.ndarray, ExtensionArray)):
+            # Similar to check_value_size, but we do not mask here since we may
+            #  end up passing it to the super() method.
             if len(value) != len(self):
                 raise ValueError(
                     f"Length of 'value' does not match. Got ({len(value)}) "

pandas/core/common.py

@@ -33,11 +33,9 @@
 
 from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
 from pandas.core.dtypes.common import (
-    is_array_like,
     is_bool_dtype,
     is_integer,
 )
-from pandas.core.dtypes.dtypes import ExtensionDtype
 from pandas.core.dtypes.generic import (
     ABCExtensionArray,
     ABCIndex,
@@ -120,9 +118,7 @@ def is_bool_indexer(key: Any) -> bool:
     check_array_indexer : Check that `key` is a valid array to index,
         and convert to an ndarray.
     """
-    if isinstance(key, (ABCSeries, np.ndarray, ABCIndex)) or (
-        is_array_like(key) and isinstance(key.dtype, ExtensionDtype)
-    ):
+    if isinstance(key, (ABCSeries, np.ndarray, ABCIndex, ABCExtensionArray)):
         if key.dtype == np.object_:
             key_array = np.asarray(key)
 
@@ -420,7 +416,7 @@ def random_state(state: np.random.Generator) -> np.random.Generator:
 
 @overload
 def random_state(
-    state: int | ArrayLike | np.random.BitGenerator | np.random.RandomState | None,
+    state: int | np.ndarray | np.random.BitGenerator | np.random.RandomState | None,
 ) -> np.random.RandomState:
     ...
 
@@ -445,24 +441,8 @@ def random_state(state: RandomState | None = None):
     np.random.RandomState or np.random.Generator. If state is None, returns np.random
 
     """
-    if (
-        is_integer(state)
-        or is_array_like(state)
-        or isinstance(state, np.random.BitGenerator)
-    ):
-        # error: Argument 1 to "RandomState" has incompatible type "Optional[Union[int,
-        # Union[ExtensionArray, ndarray[Any, Any]], Generator, RandomState]]"; expected
-        # "Union[None, Union[Union[_SupportsArray[dtype[Union[bool_, integer[Any]]]],
-        # Sequence[_SupportsArray[dtype[Union[bool_, integer[Any]]]]],
-        # Sequence[Sequence[_SupportsArray[dtype[Union[bool_, integer[Any]]]]]],
-        # Sequence[Sequence[Sequence[_SupportsArray[dtype[Union[bool_,
-        # integer[Any]]]]]]],
-        # Sequence[Sequence[Sequence[Sequence[_SupportsArray[dtype[Union[bool_,
-        # integer[Any]]]]]]]]], Union[bool, int, Sequence[Union[bool, int]],
-        # Sequence[Sequence[Union[bool, int]]], Sequence[Sequence[Sequence[Union[bool,
-        # int]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int]]]]]]],
-        # BitGenerator]"
-        return np.random.RandomState(state)  # type: ignore[arg-type]
+    if is_integer(state) or isinstance(state, (np.ndarray, np.random.BitGenerator)):
+        return np.random.RandomState(state)
     elif isinstance(state, np.random.RandomState):
         return state
     elif isinstance(state, np.random.Generator):