resolve flake8 conflicts

Author: guenteru

pandas/core/groupby/groupby.py

@@ -2300,14 +2300,14 @@ def levels(self):
     def names(self):
         # GH 19029
         # add suffix to level name in case they contain duplicates (GH 19029):
-        orig_names =  [ping.name for ping in self.groupings]
-        # if no names were assigned return the original names 
+        orig_names = [ping.name for ping in self.groupings]
+        # if no names were assigned return the original names
         if all(x is None for x in orig_names):
             return orig_names
         # in case duplicates are contained rename all of them
         if len(set(orig_names)) < len(orig_names):
-            orig_names = [''.join([str(x),str(i)])
-                                for i,x in enumerate(orig_names)]
+            orig_names = [''.join([str(x), str(i)])
+                          for i, x in enumerate(orig_names)]
 
         return orig_names
 

pandas/tests/groupby/test_categorical.py

@@ -559,14 +559,12 @@ def test_as_index():
     tm.assert_frame_equal(result, expected)
 
     # GH 19029: conflicitng names should not raise a value error anymore
-    raised=False
+    raised = False
     try:
         df.groupby(['cat', s.rename('cat')], observed=True).sum()
     except ValueError as e:
         raised = True
-    assert raised == False
-        
-         
+    assert raised is False
 
     # is original index dropped?
     group_columns = ['cat', 'A']

pandas/tests/groupby/test_groupby.py

@@ -1677,9 +1677,9 @@ def test_tuple_correct_keyerror():
 
 
 def test_dup_index_names():
-    # duplicated index names in groupby operations should be renamed (GH 19029):
-    df = pd.DataFrame(data={'date': list(pd.date_range('5.1.2018', '5.3.2018')),
-                            'vals': list(range(3))})
+    # dup. index names in groupby operations should be renamed (GH 19029):
+    df = pd.DataFrame({'date': list(pd.date_range('5.1.2018', '5.3.2018')),
+                       'vals': list(range(3))})
 
     mi = pd.MultiIndex.from_product([[5], [1, 2, 3]], names=['date0', 'date1'])
     expected = pd.Series(data=list(range(3)), index=mi, name='vals')
@@ -1690,27 +1690,26 @@ def test_dup_index_names():
     except ValueError as e:
         failed = True
 
-    assert failed == False
+    assert failed is False
 
-    tm.assert_series_equal(result,expected)
+    tm.assert_series_equal(result, expected)
 
 
 def test_empty_index_names():
     # don't rename frames in case no names were assigned (GH 19029)
-    df = pd.DataFrame(data={'date': list(pd.date_range('5.1.2018', '5.3.2018')),
-                            'vals': list(range(3))})
+    df = pd.DataFrame({'date': list(pd.date_range('5.1.2018', '5.3.2018')),
+                       'vals': list(range(3))})
 
     mi = pd.MultiIndex.from_product([[5], [1, 2, 3]])
     expected = pd.Series(data=list(range(3)), index=mi, name='vals')
 
     failed = False
     try:
         result = df.groupby([df.date.dt.month.rename(None),
-                        df.date.dt.day.rename(None)])['vals'].sum()
+                             df.date.dt.day.rename(None)])['vals'].sum()
     except ValueError as e:
         failed = True
 
-    assert failed == False
-
-    tm.assert_series_equal(result,expected)
+    assert failed is False
 
+    tm.assert_series_equal(result, expected)

pandas/tests/reshape/test_pivot.py

@@ -1709,16 +1709,17 @@ def test_crosstab_dup_index_names(self):
         s = pd.Series(range(3), name='foo')
         failed = False
         try:
-           result=pd.crosstab(s,s)
+            result = pd.crosstab(s, s)
         except ValueError as e:
             failed = True
 
-        assert failed == False
+        assert failed is False
 
-        s0 = pd.Series(range(3),name='foo0')
-        s1 = pd.Series(range(3),name='foo1')
-        expected = pd.DataFrame(data=np.diag(np.ones(3,dtype='int64')), index=s0, columns=s1)
-        tm.assert_frame_equal(result,expected)
+        s0 = pd.Series(range(3), name='foo0')
+        s1 = pd.Series(range(3), name='foo1')
+        expected = pd.DataFrame(np.diag(np.ones(3, dtype='int64')),
+                                index=s0, columns=s1)
+        tm.assert_frame_equal(result, expected)
 
     @pytest.mark.parametrize("names", [['a', ('b', 'c')],
                                        [('a', 'b'), 'c']])