Using pytest parametrize as per @jreback suggestion.

Author: Jean-Mathieu Deschenes

pandas/tests/indexing/test_timedelta.py

@@ -1,3 +1,5 @@
+import pytest
+
 import pandas as pd
 from pandas.util import testing as tm
 
@@ -20,20 +22,22 @@ def test_boolean_indexing(self):
                                     dtype='int64')
             tm.assert_frame_equal(expected, result)
 
-    def test_list_like_indexing(self):
+    @pytest.mark.parametrize("indexer, expected",
+                             [(0, [20, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
+                              (slice(4, 8), [0, 1, 2, 3, 20, 20, 20, 20, 8, 9],),
+                              ([3, 5], [0, 1, 2, 20, 4, 20, 6, 7, 8, 9])])
+    def test_list_like_indexing(self, indexer, expected):
         # GH 16637
         df = pd.DataFrame({'x': range(10)}, dtype="int64")
         df.index = pd.to_timedelta(range(10), unit='s')
+        try:
+            df.loc[df.index[indexer], 'x'] = 20
+        except ValueError:
+            raise ValueError(indexer)
 
-        conditions = [df.index[0], df.index[4:8], df.index[[3, 5]]]
-        expected_data = [[20, 1, 2, 3, 4, 5, 6, 7, 8, 9],
-                         [0, 1, 2, 3, 20, 20, 20, 20, 8, 9],
-                         [0, 1, 2, 20, 4, 20, 6, 7, 8, 9]]
-        for cond, data in zip(conditions, expected_data):
-            result = df.copy()
-            result.loc[cond, 'x'] = 20
-            expected = pd.DataFrame(data,
-                                    index=pd.to_timedelta(range(10), unit='s'),
-                                    columns=['x'],
-                                    dtype="int64")
-            tm.assert_frame_equal(expected, result)
+        expected = pd.DataFrame(expected,
+                                index=pd.to_timedelta(range(10), unit='s'),
+                                columns=['x'],
+                                dtype="int64")
+
+        tm.assert_frame_equal(expected, df)