Adjust dummies tests for new string option (pandas-dev#56527)

Author: phofl

pandas/tests/reshape/test_from_dummies.py

@@ -328,9 +328,13 @@ def test_no_prefix_string_cats_contains_get_dummies_NaN_column():
         ),
     ],
 )
-def test_no_prefix_string_cats_default_category(default_category, expected):
+def test_no_prefix_string_cats_default_category(
+    default_category, expected, using_infer_string
+):
     dummies = DataFrame({"a": [1, 0, 0], "b": [0, 1, 0]})
     result = from_dummies(dummies, default_category=default_category)
+    if using_infer_string:
+        expected[""] = expected[""].astype("string[pyarrow_numpy]")
     tm.assert_frame_equal(result, expected)
 
 

pandas/tests/reshape/test_get_dummies.py

@@ -79,7 +79,7 @@ def test_get_dummies_basic(self, sparse, dtype):
         result = get_dummies(s_series_index, sparse=sparse, dtype=dtype)
         tm.assert_frame_equal(result, expected)
 
-    def test_get_dummies_basic_types(self, sparse, dtype):
+    def test_get_dummies_basic_types(self, sparse, dtype, using_infer_string):
         # GH 10531
         s_list = list("abc")
         s_series = Series(s_list)
@@ -120,7 +120,8 @@ def test_get_dummies_basic_types(self, sparse, dtype):
 
         result = get_dummies(s_df, columns=["a"], sparse=sparse, dtype=dtype)
 
-        expected_counts = {"int64": 1, "object": 1}
+        key = "string" if using_infer_string else "object"
+        expected_counts = {"int64": 1, key: 1}
         expected_counts[dtype_name] = 3 + expected_counts.get(dtype_name, 0)
 
         expected = Series(expected_counts, name="count").sort_index()
@@ -213,7 +214,7 @@ def test_dataframe_dummies_all_obj(self, df, sparse):
 
         tm.assert_frame_equal(result, expected)
 
-    def test_dataframe_dummies_string_dtype(self, df):
+    def test_dataframe_dummies_string_dtype(self, df, using_infer_string):
         # GH44965
         df = df[["A", "B"]]
         df = df.astype({"A": "object", "B": "string"})
@@ -227,7 +228,9 @@ def test_dataframe_dummies_string_dtype(self, df):
             },
             dtype=bool,
         )
-        expected[["B_b", "B_c"]] = expected[["B_b", "B_c"]].astype("boolean")
+        if not using_infer_string:
+            # infer_string returns numpy bools
+            expected[["B_b", "B_c"]] = expected[["B_b", "B_c"]].astype("boolean")
         tm.assert_frame_equal(result, expected)
 
     def test_dataframe_dummies_mix_default(self, df, sparse, dtype):