Fix copyrights.

python/cudf/cudf/core/_base_index.py

@@ -1347,13 +1347,14 @@ def isin(self, values):
         array([ True, False, False])
         """
 
-        # Even though only list-like objects are supposed to be passed, only
-        # scalars throw errors. Other types (like dicts) just transparently
-        # return False (see the implementation of ColumnBase.isin).
+        # To match pandas behavior, even though only list-like objects are
+        # supposed to be passed, only scalars throw errors. Other types (like
+        # dicts) just transparently return False (see the implementation of
+        # ColumnBase.isin).
         if is_scalar(values):
             raise TypeError(
                 "only list-like objects are allowed to be passed "
-                f"to isin(), you passed a [{type(values).__name__}]"
+                f"to isin(), you passed a {type(values).__name__}"
             )
 
         return self._values.isin(values).values

python/cudf/cudf/core/dataframe.py

@@ -5173,15 +5173,15 @@ def isin(self, values):
         # TODO: propagate nulls through isin
         # https://github.com/rapidsai/cudf/issues/7556
 
+        fill_value = cudf.Scalar(False)
+
         def make_false_column_like_self():
-            return column.full(size=len(self), fill_value=False, dtype="bool")
+            return column.full(len(self), fill_value, "bool")
 
         # Preprocess different input types into a mapping from column names to
         # a list of values to check.
         result = {}
-        if isinstance(values, (Series, DataFrame)):
-            obj_dtype = cudf.dtype("object")
-
+        if isinstance(values, IndexedFrame):
             # Note: In the case where values is a Series, computing some
             # information about the values column outside the loop may result
             # in performance gains.  However, since categorical conversion
@@ -5213,16 +5213,14 @@ def make_false_column_like_self():
                     # because if only one was categorical then it's already
                     # been converted and we have to check if they're strings.
                     if self_is_cat and other_is_cat:
-                        self_is_obj = other_is_obj = False
+                        self_is_str = other_is_str = False
                     else:
                         # These checks must happen after the conversions above
                         # since numpy can't handle categorical dtypes.
-                        self_is_obj = np.issubdtype(self_col.dtype, obj_dtype)
-                        other_is_obj = np.issubdtype(
-                            other_col.dtype, obj_dtype
-                        )
+                        self_is_str = is_string_dtype(self_col.dtype)
+                        other_is_str = is_string_dtype(other_col.dtype)
 
-                    if self_is_obj != other_is_obj:
+                    if self_is_str != other_is_str:
                         # Strings can't compare to anything else.
                         result[col] = make_false_column_like_self()
                     else:

python/cudf/cudf/tests/test_index.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2018-2021, NVIDIA CORPORATION.
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
 
 """
 Test related to Index

python/cudf/cudf/tests/test_series.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
 
 import operator
 import re