TYP: EA.isin

pandas/core/arrays/arrow/array.py

@@ -1023,7 +1023,7 @@ def fillna(
 
         return super().fillna(value=value, method=method, limit=limit, copy=copy)
 
-    def isin(self, values) -> npt.NDArray[np.bool_]:
+    def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
         # short-circuit to return all False array.
         if not len(values):
             return np.zeros(len(self), dtype=bool)

pandas/core/arrays/base.py

@@ -1355,15 +1355,15 @@ def equals(self, other: object) -> bool:
             equal_na = self.isna() & other.isna()  # type: ignore[operator]
             return bool((equal_values | equal_na).all())
 
-    def isin(self, values) -> npt.NDArray[np.bool_]:
+    def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
         """
         Pointwise comparison for set containment in the given values.
 
         Roughly equivalent to `np.array([x in values for x in self])`
 
         Parameters
         ----------
-        values : Sequence
+        values : np.ndarray or ExtensionArray
 
         Returns
         -------

pandas/core/arrays/categorical.py

@@ -2570,7 +2570,7 @@ def describe(self) -> DataFrame:
 
         return result
 
-    def isin(self, values) -> npt.NDArray[np.bool_]:
+    def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
         """
         Check whether `values` are contained in Categorical.
 
@@ -2580,7 +2580,7 @@ def isin(self, values) -> npt.NDArray[np.bool_]:
 
         Parameters
         ----------
-        values : set or list-like
+        values : np.ndarray or ExtensionArray
             The sequence of values to test. Passing in a single string will
             raise a ``TypeError``. Instead, turn a single string into a
             list of one element.
@@ -2611,13 +2611,6 @@ def isin(self, values) -> npt.NDArray[np.bool_]:
         >>> s.isin(['lama'])
         array([ True, False,  True, False,  True, False])
         """
-        if not is_list_like(values):
-            values_type = type(values).__name__
-            raise TypeError(
-                "only list-like objects are allowed to be passed "
-                f"to isin(), you passed a `{values_type}`"
-            )
-        values = sanitize_array(values, None, None)
         null_mask = np.asarray(isna(values))
         code_values = self.categories.get_indexer_for(values)
         code_values = code_values[null_mask | (code_values >= 0)]

pandas/core/arrays/datetimelike.py

@@ -734,22 +734,19 @@ def map(self, mapper, na_action=None):
         else:
             return result.array
 
-    def isin(self, values) -> npt.NDArray[np.bool_]:
+    def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
         """
         Compute boolean array of whether each value is found in the
         passed set of values.
 
         Parameters
         ----------
-        values : set or sequence of values
+        values : np.ndarray or ExtensionArray
 
         Returns
         -------
         ndarray[bool]
         """
-        if not hasattr(values, "dtype"):
-            values = np.asarray(values)
-
         if values.dtype.kind in "fiuc":
             # TODO: de-duplicate with equals, validate_comparison_value
             return np.zeros(self.shape, dtype=bool)
@@ -781,15 +778,22 @@ def isin(self, values) -> npt.NDArray[np.bool_]:
 
         if self.dtype.kind in "mM":
             self = cast("DatetimeArray | TimedeltaArray", self)
-            values = values.as_unit(self.unit)
+            # error: Item "ExtensionArray" of "ExtensionArray | ndarray[Any, Any]"
+            # has no attribute "as_unit"
+            values = values.as_unit(self.unit)  # type: ignore[union-attr]
 
         try:
-            self._check_compatible_with(values)
+            # error: Argument 1 to "_check_compatible_with" of "DatetimeLikeArrayMixin"
+            # has incompatible type "ExtensionArray | ndarray[Any, Any]"; expected
+            # "Period | Timestamp | Timedelta | NaTType"
+            self._check_compatible_with(values)  # type: ignore[arg-type]
         except (TypeError, ValueError):
             # Includes tzawareness mismatch and IncompatibleFrequencyError
             return np.zeros(self.shape, dtype=bool)
 
-        return isin(self.asi8, values.asi8)
+        # error: Item "ExtensionArray" of "ExtensionArray | ndarray[Any, Any]"
+        # has no attribute "asi8"
+        return isin(self.asi8, values.asi8)  # type: ignore[union-attr]
 
     # ------------------------------------------------------------------
     # Null Handling

pandas/core/arrays/interval.py

@@ -1789,12 +1789,8 @@ def contains(self, other):
             other < self._right if self.open_right else other <= self._right
         )
 
-    def isin(self, values) -> npt.NDArray[np.bool_]:
-        if not hasattr(values, "dtype"):
-            values = np.array(values)
-        values = extract_array(values, extract_numpy=True)
-
-        if isinstance(values.dtype, IntervalDtype):
+    def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
+        if isinstance(values, IntervalArray):
             if self.closed != values.closed:
                 # not comparable -> no overlap
                 return np.zeros(self.shape, dtype=bool)

pandas/core/arrays/masked.py

@@ -955,7 +955,7 @@ def take(
 
     # error: Return type "BooleanArray" of "isin" incompatible with return type
     # "ndarray" in supertype "ExtensionArray"
-    def isin(self, values) -> BooleanArray:  # type: ignore[override]
+    def isin(self, values: ArrayLike) -> BooleanArray:  # type: ignore[override]
         from pandas.core.arrays import BooleanArray
 
         # algorithms.isin will eventually convert values to an ndarray, so no extra

pandas/core/arrays/string_arrow.py

@@ -54,6 +54,7 @@
     from collections.abc import Sequence
 
     from pandas._typing import (
+        ArrayLike,
         AxisInt,
         Dtype,
         Scalar,
@@ -212,7 +213,7 @@ def _maybe_convert_setitem_value(self, value):
                     raise TypeError("Scalar must be NA or str")
         return super()._maybe_convert_setitem_value(value)
 
-    def isin(self, values) -> npt.NDArray[np.bool_]:
+    def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
         value_set = [
             pa_scalar.as_py()
             for pa_scalar in [pa.scalar(value, from_pandas=True) for value in values]