Adjust tests in util folder for new string option (pandas-dev#56181)

Author: phofl

pandas/tests/util/test_assert_frame_equal.py

@@ -109,12 +109,16 @@ def test_empty_dtypes(check_dtype):
 
 
 @pytest.mark.parametrize("check_like", [True, False])
-def test_frame_equal_index_mismatch(check_like, obj_fixture):
+def test_frame_equal_index_mismatch(check_like, obj_fixture, using_infer_string):
+    if using_infer_string:
+        dtype = "string"
+    else:
+        dtype = "object"
     msg = f"""{obj_fixture}\\.index are different
 
 {obj_fixture}\\.index values are different \\(33\\.33333 %\\)
-\\[left\\]:  Index\\(\\['a', 'b', 'c'\\], dtype='object'\\)
-\\[right\\]: Index\\(\\['a', 'b', 'd'\\], dtype='object'\\)
+\\[left\\]:  Index\\(\\['a', 'b', 'c'\\], dtype='{dtype}'\\)
+\\[right\\]: Index\\(\\['a', 'b', 'd'\\], dtype='{dtype}'\\)
 At positional index 2, first diff: c != d"""
 
     df1 = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}, index=["a", "b", "c"])
@@ -125,12 +129,16 @@ def test_frame_equal_index_mismatch(check_like, obj_fixture):
 
 
 @pytest.mark.parametrize("check_like", [True, False])
-def test_frame_equal_columns_mismatch(check_like, obj_fixture):
+def test_frame_equal_columns_mismatch(check_like, obj_fixture, using_infer_string):
+    if using_infer_string:
+        dtype = "string"
+    else:
+        dtype = "object"
     msg = f"""{obj_fixture}\\.columns are different
 
 {obj_fixture}\\.columns values are different \\(50\\.0 %\\)
-\\[left\\]:  Index\\(\\['A', 'B'\\], dtype='object'\\)
-\\[right\\]: Index\\(\\['A', 'b'\\], dtype='object'\\)"""
+\\[left\\]:  Index\\(\\['A', 'B'\\], dtype='{dtype}'\\)
+\\[right\\]: Index\\(\\['A', 'b'\\], dtype='{dtype}'\\)"""
 
     df1 = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}, index=["a", "b", "c"])
     df2 = DataFrame({"A": [1, 2, 3], "b": [4, 5, 6]}, index=["a", "b", "c"])

pandas/tests/util/test_assert_index_equal.py

@@ -205,14 +205,18 @@ def test_index_equal_names(name1, name2):
             tm.assert_index_equal(idx1, idx2)
 
 
-def test_index_equal_category_mismatch(check_categorical):
-    msg = """Index are different
+def test_index_equal_category_mismatch(check_categorical, using_infer_string):
+    if using_infer_string:
+        dtype = "string"
+    else:
+        dtype = "object"
+    msg = f"""Index are different
 
 Attribute "dtype" are different
 \\[left\\]:  CategoricalDtype\\(categories=\\['a', 'b'\\], ordered=False, \
-categories_dtype=object\\)
+categories_dtype={dtype}\\)
 \\[right\\]: CategoricalDtype\\(categories=\\['a', 'b', 'c'\\], \
-ordered=False, categories_dtype=object\\)"""
+ordered=False, categories_dtype={dtype}\\)"""
 
     idx1 = Index(Categorical(["a", "b"]))
     idx2 = Index(Categorical(["a", "b"], categories=["a", "b", "c"]))

pandas/tests/util/test_assert_series_equal.py

@@ -214,8 +214,18 @@ def test_series_equal_numeric_values_mismatch(rtol):
         tm.assert_series_equal(s1, s2, rtol=rtol)
 
 
-def test_series_equal_categorical_values_mismatch(rtol):
-    msg = """Series are different
+def test_series_equal_categorical_values_mismatch(rtol, using_infer_string):
+    if using_infer_string:
+        msg = """Series are different
+
+Series values are different \\(66\\.66667 %\\)
+\\[index\\]: \\[0, 1, 2\\]
+\\[left\\]:  \\['a', 'b', 'c'\\]
+Categories \\(3, string\\): \\[a, b, c\\]
+\\[right\\]: \\['a', 'c', 'b'\\]
+Categories \\(3, string\\): \\[a, b, c\\]"""
+    else:
+        msg = """Series are different
 
 Series values are different \\(66\\.66667 %\\)
 \\[index\\]: \\[0, 1, 2\\]
@@ -246,14 +256,18 @@ def test_series_equal_datetime_values_mismatch(rtol):
         tm.assert_series_equal(s1, s2, rtol=rtol)
 
 
-def test_series_equal_categorical_mismatch(check_categorical):
-    msg = """Attributes of Series are different
+def test_series_equal_categorical_mismatch(check_categorical, using_infer_string):
+    if using_infer_string:
+        dtype = "string"
+    else:
+        dtype = "object"
+    msg = f"""Attributes of Series are different
 
 Attribute "dtype" are different
 \\[left\\]:  CategoricalDtype\\(categories=\\['a', 'b'\\], ordered=False, \
-categories_dtype=object\\)
+categories_dtype={dtype}\\)
 \\[right\\]: CategoricalDtype\\(categories=\\['a', 'b', 'c'\\], \
-ordered=False, categories_dtype=object\\)"""
+ordered=False, categories_dtype={dtype}\\)"""
 
     s1 = Series(Categorical(["a", "b"]))
     s2 = Series(Categorical(["a", "b"], categories=list("abc")))