CLN: remove check_series_type

pandas/_testing.py

@@ -34,6 +34,7 @@
     is_interval_dtype,
     is_list_like,
     is_number,
+    is_numeric_dtype,
     is_period_dtype,
     is_sequence,
     is_timedelta64_dtype,
@@ -1064,7 +1065,6 @@ def assert_series_equal(
     right,
     check_dtype=True,
     check_index_type="equiv",
-    check_series_type=True,
     check_less_precise=False,
     check_names=True,
     check_exact=False,
@@ -1085,8 +1085,6 @@ def assert_series_equal(
     check_index_type : bool or {'equiv'}, default 'equiv'
         Whether to check the Index class, dtype and inferred_type
         are identical.
-    check_series_type : bool, default True
-        Whether to check the Series class is identical.
     check_less_precise : bool or int, default False
         Specify comparison precision. Only used when check_exact is False.
         5 digits (False) or 3 digits (True) after decimal points are compared.
@@ -1118,11 +1116,10 @@ def assert_series_equal(
     # instance validation
     _check_isinstance(left, right, Series)
 
-    if check_series_type:
-        # ToDo: There are some tests using rhs is sparse
-        # lhs is dense. Should use assert_class_equal in future
-        assert isinstance(left, type(right))
-        # assert_class_equal(left, right, obj=obj)
+    # TODO: There are some tests using rhs is sparse
+    # lhs is dense. Should use assert_class_equal in future
+    assert isinstance(left, type(right))
+    # assert_class_equal(left, right, obj=obj)
 
     # length comparison
     if len(left) != len(right):
@@ -1147,47 +1144,40 @@ def assert_series_equal(
         # is False. We'll still raise if only one is a `Categorical`,
         # regardless of `check_categorical`
         if (
-            is_categorical_dtype(left)
-            and is_categorical_dtype(right)
+            is_categorical_dtype(left.dtype)
+            and is_categorical_dtype(right.dtype)
             and not check_categorical
         ):
             pass
         else:
             assert_attr_equal("dtype", left, right, obj=f"Attributes of {obj}")
 
     if check_exact:
+        if not is_numeric_dtype(left.dtype):
+            raise AssertionError("check_exact may only be used with numeric Series")
+
         assert_numpy_array_equal(
-            left._internal_get_values(),
-            right._internal_get_values(),
-            check_dtype=check_dtype,
-            obj=str(obj),
+            left._values, right._values, check_dtype=check_dtype, obj=str(obj)
         )
-    elif check_datetimelike_compat:
+    elif check_datetimelike_compat and (
+        needs_i8_conversion(left.dtype) or needs_i8_conversion(right.dtype)
+    ):
         # we want to check only if we have compat dtypes
         # e.g. integer and M|m are NOT compat, but we can simply check
         # the values in that case
-        if needs_i8_conversion(left) or needs_i8_conversion(right):
-
-            # datetimelike may have different objects (e.g. datetime.datetime
-            # vs Timestamp) but will compare equal
-            if not Index(left._values).equals(Index(right._values)):
-                msg = (
-                    f"[datetimelike_compat=True] {left._values} "
-                    f"is not equal to {right._values}."
-                )
-                raise AssertionError(msg)
-        else:
-            assert_numpy_array_equal(
-                left._internal_get_values(),
-                right._internal_get_values(),
-                check_dtype=check_dtype,
+
+        # datetimelike may have different objects (e.g. datetime.datetime
+        # vs Timestamp) but will compare equal
+        if not Index(left._values).equals(Index(right._values)):
+            msg = (
+                f"[datetimelike_compat=True] {left._values} "
+                f"is not equal to {right._values}."
             )
-    elif is_interval_dtype(left) or is_interval_dtype(right):
+            raise AssertionError(msg)
+    elif is_interval_dtype(left.dtype) or is_interval_dtype(right.dtype):
         assert_interval_array_equal(left.array, right.array)
-    elif is_extension_array_dtype(left.dtype) and is_datetime64tz_dtype(left.dtype):
+    elif is_datetime64tz_dtype(left.dtype):
         # .values is an ndarray, but ._values is the ExtensionArray.
-        # TODO: Use .array
-        assert is_extension_array_dtype(right.dtype)
         assert_extension_array_equal(left._values, right._values)
     elif (
         is_extension_array_dtype(left)

pandas/tests/io/pytables/test_store.py

@@ -2312,7 +2312,7 @@ def test_index_types(self, setup_path):
             values = np.random.randn(2)
 
             func = lambda l, r: tm.assert_series_equal(
-                l, r, check_dtype=True, check_index_type=True, check_series_type=True
+                l, r, check_dtype=True, check_index_type=True
             )
 
         with catch_warnings(record=True):