diff --git a/pandas/tests/indexes/test_category.py b/pandas/tests/indexes/test_category.py
index b8c50239efac3..f2e409deb2ce4 100644
--- a/pandas/tests/indexes/test_category.py
+++ b/pandas/tests/indexes/test_category.py
@@ -348,7 +348,8 @@ def test_astype(self):
                                        right=[2, 4],
                                        closed='right')
 
-        ci = CategoricalIndex(Categorical.from_codes([0, 1, -1], categories=ii, ordered=True))
+        ci = CategoricalIndex(Categorical.from_codes(
+            [0, 1, -1], categories=ii, ordered=True))
 
         result = ci.astype('interval')
         expected = ii.take([0, 1, -1])
diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py
index 8600b2d726e49..dd370f0a20c2e 100644
--- a/pandas/tests/test_categorical.py
+++ b/pandas/tests/test_categorical.py
@@ -1608,11 +1608,12 @@ def setUp(self):
         self.factor = Categorical(['a', 'b', 'b', 'a', 'a', 'c', 'c', 'c'])
 
         df = DataFrame({'value': np.random.randint(0, 10000, 100)})
-        labels = [ "{0} - {1}".format(i, i + 499) for i in range(0, 10000, 500) ]
+        labels = ["{0} - {1}".format(i, i + 499) for i in range(0, 10000, 500)]
         cat_labels = Categorical(labels, labels)
 
         df = df.sort_values(by=['value'], ascending=True)
-        df['value_group'] = pd.cut(df.value, range(0, 10500, 500), right=False, labels=cat_labels)
+        df['value_group'] = pd.cut(df.value, range(0, 10500, 500),
+                                   right=False, labels=cat_labels)
         self.cat = df
 
     def test_dtypes(self):
@@ -2019,8 +2020,10 @@ def test_series_functions_no_warnings(self):
 
     def test_assignment_to_dataframe(self):
         # assignment
-        df = DataFrame({'value': np.array(np.random.randint(0, 10000, 100),dtype='int32')})
-        labels = Categorical(["{0} - {1}".format(i, i + 499) for i in range(0, 10000, 500)])
+        df = DataFrame({'value': np.array(
+            np.random.randint(0, 10000, 100), dtype='int32')})
+        labels = Categorical(["{0} - {1}".format(i, i + 499)
+                              for i in range(0, 10000, 500)])
 
         df = df.sort_values(by=['value'], ascending=True)
         s = pd.cut(df.value, range(0, 10500, 500), right=False, labels=labels)
@@ -3134,7 +3137,7 @@ def test_slicing(self):
         df = DataFrame({'value': (np.arange(100) + 1).astype('int64')})
         df['D'] = pd.cut(df.value, bins=[0, 25, 50, 75, 100])
 
-        expected = Series([11, Interval(0, 25)], index=['value','D'], name=10)
+        expected = Series([11, Interval(0, 25)], index=['value', 'D'], name=10)
         result = df.iloc[10]
         tm.assert_series_equal(result, expected)
 
@@ -3144,7 +3147,7 @@ def test_slicing(self):
         result = df.iloc[10:20]
         tm.assert_frame_equal(result, expected)
 
-        expected = Series([9, Interval(0, 25)],index=['value', 'D'], name=8)
+        expected = Series([9, Interval(0, 25)], index=['value', 'D'], name=8)
         result = df.loc[8]
         tm.assert_series_equal(result, expected)