remove Categorical name warnings from tests, xref #10482

Author: jreback

pandas/tests/test_categorical.py

@@ -1207,6 +1207,8 @@ def test_deprecated_levels(self):
         self.assertFalse(LooseVersion(pd.__version__) >= '0.18')
 
     def test_removed_names_produces_warning(self):
+
+        # 10482
         with tm.assert_produces_warning(UserWarning):
             Categorical([0,1], name="a")
 
@@ -3580,9 +3582,13 @@ def test_cat_accessor_api(self):
         self.assertFalse(hasattr(invalid, 'cat'))
 
     def test_pickle_v0_14_1(self):
-        cat = pd.Categorical(values=['a', 'b', 'c'],
-                             categories=['a', 'b', 'c', 'd'],
-                             name='foobar', ordered=False)
+
+        # we have the name warning
+        # 10482
+        with tm.assert_produces_warning(UserWarning):
+            cat = pd.Categorical(values=['a', 'b', 'c'],
+                                 categories=['a', 'b', 'c', 'd'],
+                                 name='foobar', ordered=False)
         pickle_path = os.path.join(tm.get_data_path(),
                                    'categorical_0_14_1.pickle')
         # This code was executed once on v0.14.1 to generate the pickle:
@@ -3597,9 +3603,12 @@ def test_pickle_v0_15_2(self):
         # ordered -> _ordered
         # GH 9347
 
-        cat = pd.Categorical(values=['a', 'b', 'c'],
-                             categories=['a', 'b', 'c', 'd'],
-                             name='foobar', ordered=False)
+        # we have the name warning
+        # 10482
+        with tm.assert_produces_warning(UserWarning):
+            cat = pd.Categorical(values=['a', 'b', 'c'],
+                                 categories=['a', 'b', 'c', 'd'],
+                                 name='foobar', ordered=False)
         pickle_path = os.path.join(tm.get_data_path(),
                                    'categorical_0_15_2.pickle')
         # This code was executed once on v0.15.2 to generate the pickle:

pandas/tests/test_groupby.py

@@ -3570,7 +3570,7 @@ def test_groupby_categorical_index(self):
 
         levels = ['foo', 'bar', 'baz', 'qux']
         codes = np.random.randint(0, 4, size=20)
-        cats = Categorical.from_codes(codes, levels, name='myfactor', ordered=True)
+        cats = Categorical.from_codes(codes, levels, ordered=True)
         df = DataFrame(np.repeat(np.arange(20),4).reshape(-1,4), columns=list('abcd'))
         df['cats'] = cats