BUG: Fix user-facing AssertionError with to_html (#25608) (#25620)

Author: ArtificialQualia

doc/source/whatsnew/v0.25.0.rst

@@ -214,7 +214,7 @@ I/O
 - Bug in :func:`read_json` for ``orient='table'`` when it tries to infer dtypes by default, which is not applicable as dtypes are already defined in the JSON schema (:issue:`21345`)
 - Bug in :func:`read_json` for ``orient='table'`` and float index, as it infers index dtype by default, which is not applicable because index dtype is already defined in the JSON schema (:issue:`25433`)
 - Bug in :func:`read_json` for ``orient='table'`` and string of float column names, as it makes a column name type conversion to Timestamp, which is not applicable because column names are already defined in the JSON schema (:issue:`25435`)
--
+- :meth:`DataFrame.to_html` now raises ``TypeError`` when using an invalid type for the ``classes`` parameter instead of ``AsseertionError`` (:issue:`25608`)
 -
 -
 

pandas/io/formats/html.py

@@ -163,8 +163,8 @@ def _write_table(self, indent=0):
             if isinstance(self.classes, str):
                 self.classes = self.classes.split()
             if not isinstance(self.classes, (list, tuple)):
-                raise AssertionError('classes must be list or tuple, not {typ}'
-                                     .format(typ=type(self.classes)))
+                raise TypeError('classes must be a string, list, or tuple, '
+                                'not {typ}'.format(typ=type(self.classes)))
             _classes.extend(self.classes)
 
         if self.table_id is None:

pandas/tests/io/formats/test_to_html.py

@@ -623,3 +623,13 @@ def test_ignore_display_max_colwidth(method, expected, max_colwidth):
         result = getattr(df, method)()
     expected = expected(max_colwidth)
     assert expected in result
+
+
+@pytest.mark.parametrize("classes", [True, 0])
+def test_to_html_invalid_classes_type(classes):
+    # GH 25608
+    df = DataFrame()
+    msg = "classes must be a string, list, or tuple"
+
+    with pytest.raises(TypeError, match=msg):
+        df.to_html(classes=classes)