adjust for comments II

pandas/tests/frame/test_alter_axes.py

@@ -979,8 +979,7 @@ def test_reset_index(self, float_frame):
         ):
             values = lev.take(level_codes)
             name = names[i]
-            tm.assert_index_equal(values, Index(deleveled[name]), check_names=False)
-            assert values.name is None
+            tm.assert_index_equal(values, Index(deleveled[name].rename(name=None)))
 
         stacked.index.names = [None, None]
         deleveled2 = stacked.reset_index()

pandas/tests/indexes/multi/test_names.py

@@ -79,9 +79,8 @@ def test_copy_names():
 def test_names(idx, index_names):
 
     # names are assigned in setup
-    names = index_names
+    assert index_names == ["first", "second"]
     level_names = [level.name for level in idx.levels]
-    assert names == ["first", "second"]
     assert level_names == [None, None]
 
     # setting bad names on existing
@@ -107,7 +106,7 @@ def test_names(idx, index_names):
             names=["first", "second", "third"],
         )
 
-    # names are assigned
+    # names are assigned on index, but not transferred to the levels
     index.names = ["a", "b"]
     level_names = [level.name for level in index.levels]
     assert level_names == [None, None]

pandas/tests/indexes/multi/test_reindex.py

@@ -6,19 +6,17 @@
 import pandas.util.testing as tm
 
 
-def check_level_names(index, names):
-    assert [level.name for level in index.levels] == list(names)
-
-
 def test_reindex(idx):
     result, indexer = idx.reindex(list(idx[:4]))
     assert isinstance(result, MultiIndex)
-    check_level_names(result, [None, None])
+    assert result.names == ["first", "second"]
+    assert [level.name for level in result.levels] == [None, None]
 
     result, indexer = idx.reindex(list(idx))
     assert isinstance(result, MultiIndex)
     assert indexer is None
-    check_level_names(result, [None, None])
+    assert result.names == ["first", "second"]
+    assert [level.name for level in result.levels] == [None, None]
 
 
 def test_reindex_level(idx):

pandas/tests/reshape/test_reshape.py

@@ -617,16 +617,15 @@ def test_reshaping_multi_index_categorical(self):
         df.index.names = ["major", "minor"]
         df["str"] = "foo"
 
-        dti = df.index.levels[0].set_names(["major"])
-
         df["category"] = df["str"].astype("category")
         result = df["category"].unstack()
 
+        dti = df.index.levels[0]
         c = Categorical(["foo"] * len(dti))
         expected = DataFrame(
             {"A": c.copy(), "B": c.copy(), "C": c.copy(), "D": c.copy()},
             columns=Index(list("ABCD"), name="minor"),
-            index=dti,
+            index=dti.rename("major"),
         )
         tm.assert_frame_equal(result, expected)
 

pandas/tests/test_multilevel.py

@@ -996,6 +996,8 @@ def test_frame_group_ops(self, op, level, axis, skipna, sort):
         self.frame.iloc[1, [1, 2]] = np.nan
         self.frame.iloc[7, [0, 1]] = np.nan
 
+        level_name = self.frame.index.names[level]
+
         if axis == 0:
             frame = self.frame
         else:
@@ -1016,12 +1018,12 @@ def aggf(x):
             frame = frame.sort_index(level=level, axis=axis)
 
         # for good measure, groupby detail
-        level_index = frame._get_axis(axis).levels[level]
+        level_index = frame._get_axis(axis).levels[level].rename(level_name)
 
-        tm.assert_index_equal(leftside._get_axis(axis), level_index, check_names=False)
-        tm.assert_index_equal(rightside._get_axis(axis), level_index, check_names=False)
+        tm.assert_index_equal(leftside._get_axis(axis), level_index)
+        tm.assert_index_equal(rightside._get_axis(axis), level_index)
 
-        tm.assert_frame_equal(leftside, rightside, check_names=False)
+        tm.assert_frame_equal(leftside, rightside)
 
     def test_stat_op_corner(self):
         obj = Series([10.0], index=MultiIndex.from_tuples([(2, 3)]))
@@ -1607,12 +1609,18 @@ def test_constructor_with_tz(self):
         )
 
         result = MultiIndex.from_arrays([index, columns])
-        tm.assert_index_equal(result.levels[0], index, check_names=False)
-        tm.assert_index_equal(result.levels[1], columns, check_names=False)
+
+        assert result.names == ["dt1", "dt2"]
+        # levels don't have names set, so set name of index/columns to None in checks
+        tm.assert_index_equal(result.levels[0], index.rename(name=None))
+        tm.assert_index_equal(result.levels[1], columns.rename(name=None))
 
         result = MultiIndex.from_arrays([Series(index), Series(columns)])
-        tm.assert_index_equal(result.levels[0], index, check_names=False)
-        tm.assert_index_equal(result.levels[1], columns, check_names=False)
+
+        assert result.names == ["dt1", "dt2"]
+        # levels don't have names set, so set name of index/columns to None in checks
+        tm.assert_index_equal(result.levels[0], index.rename(name=None))
+        tm.assert_index_equal(result.levels[1], columns.rename(name=None))
 
     def test_set_index_datetime(self):
         # GH 3950
@@ -1634,19 +1642,18 @@ def test_set_index_datetime(self):
         df.index = df.index.tz_convert("US/Pacific")
 
         expected = pd.DatetimeIndex(
-            ["2011-07-19 07:00:00", "2011-07-19 08:00:00", "2011-07-19 09:00:00"],
-            name="datetime",
+            ["2011-07-19 07:00:00", "2011-07-19 08:00:00", "2011-07-19 09:00:00"]
         )
         expected = expected.tz_localize("UTC").tz_convert("US/Pacific")
 
         df = df.set_index("label", append=True)
-        tm.assert_index_equal(df.index.levels[0], expected, check_names=False)
+        tm.assert_index_equal(df.index.levels[0], expected)
         tm.assert_index_equal(df.index.levels[1], Index(["a", "b"]))
         assert df.index.names == ["datetime", "label"]
 
         df = df.swaplevel(0, 1)
         tm.assert_index_equal(df.index.levels[0], Index(["a", "b"]))
-        tm.assert_index_equal(df.index.levels[1], expected, check_names=False)
+        tm.assert_index_equal(df.index.levels[1], expected)
         assert df.index.names == ["label", "datetime"]
 
         df = DataFrame(np.random.random(6))