fixup

Author: jorisvandenbossche

pandas/tests/indexes/datetimes/test_ops.py

@@ -389,27 +389,27 @@ def test_comp_nat(self):
                                  pd.Timestamp('2011-01-03')])
         right = pd.DatetimeIndex([pd.NaT, pd.NaT, pd.Timestamp('2011-01-03')])
 
-        for l, r in [(left, right),
-                     (left.astype(object), right.astype(object))]:
-            result = r == l
+        for lhs, rhs in [(left, right),
+                         (left.astype(object), right.astype(object))]:
+            result = rhs == lhs
             expected = np.array([False, False, True])
             tm.assert_numpy_array_equal(result, expected)
 
-            result = l != r
+            result = lhs != rhs
             expected = np.array([True, True, False])
             tm.assert_numpy_array_equal(result, expected)
 
             expected = np.array([False, False, False])
-            tm.assert_numpy_array_equal(l == pd.NaT, expected)
-            tm.assert_numpy_array_equal(pd.NaT == r, expected)
+            tm.assert_numpy_array_equal(lhs == pd.NaT, expected)
+            tm.assert_numpy_array_equal(pd.NaT == rhs, expected)
 
             expected = np.array([True, True, True])
-            tm.assert_numpy_array_equal(l != pd.NaT, expected)
-            tm.assert_numpy_array_equal(pd.NaT != l, expected)
+            tm.assert_numpy_array_equal(lhs != pd.NaT, expected)
+            tm.assert_numpy_array_equal(pd.NaT != lhs, expected)
 
             expected = np.array([False, False, False])
-            tm.assert_numpy_array_equal(l < pd.NaT, expected)
-            tm.assert_numpy_array_equal(pd.NaT > l, expected)
+            tm.assert_numpy_array_equal(lhs < pd.NaT, expected)
+            tm.assert_numpy_array_equal(pd.NaT > lhs, expected)
 
     def test_value_counts_unique(self):
         # GH 7735

pandas/tests/indexes/period/test_ops.py

@@ -27,7 +27,7 @@ def test_ops_properties(self):
         self.check_ops_properties(PeriodIndex._object_ops, f)
         self.check_ops_properties(PeriodIndex._bool_ops, f)
 
-    def test_astype(self):
+    def test_astype_object(self):
         idx = pd.period_range(start='2013-01-01', periods=4, freq='M',
                               name='idx')
         expected_list = [pd.Period('2013-01-31', freq='M'),
@@ -291,7 +291,7 @@ def test_comp_nat(self):
         right = pd.PeriodIndex([pd.NaT, pd.NaT, pd.Period('2011-01-03')])
 
         for lhs, rhs in [(left, right),
-                     (left.astype(object), right.astype(object))]:
+                         (left.astype(object), right.astype(object))]:
             result = lhs == rhs
             expected = np.array([False, False, True])
             tm.assert_numpy_array_equal(result, expected)

pandas/tests/indexes/period/test_period.py

@@ -380,7 +380,7 @@ def test_factorize(self):
         tm.assert_numpy_array_equal(arr, exp_arr)
         tm.assert_index_equal(idx, exp_idx)
 
-    def test_astype(self):
+    def test_astype_object(self):
         idx = pd.PeriodIndex([], freq='M')
 
         exp = np.array([], dtype=object)

pandas/tests/indexes/timedeltas/test_ops.py

@@ -25,7 +25,7 @@ def test_ops_properties(self):
         self.check_ops_properties(TimedeltaIndex._field_ops, f)
         self.check_ops_properties(TimedeltaIndex._object_ops, f)
 
-    def test_astype(self):
+    def test_astype_object(self):
         idx = timedelta_range(start='1 days', periods=4, freq='D', name='idx')
         expected_list = [Timedelta('1 days'), Timedelta('2 days'),
                          Timedelta('3 days'), Timedelta('4 days')]
@@ -217,27 +217,27 @@ def test_comp_nat(self):
                                   pd.Timedelta('3 days')])
         right = pd.TimedeltaIndex([pd.NaT, pd.NaT, pd.Timedelta('3 days')])
 
-        for l, r in [(left, right),
-                     (left.astype(object), right.astype(object))]:
-            result = r == l
+        for lhs, rhs in [(left, right),
+                         (left.astype(object), right.astype(object))]:
+            result = rhs == lhs
             expected = np.array([False, False, True])
             tm.assert_numpy_array_equal(result, expected)
 
-            result = r != l
+            result = rhs != lhs
             expected = np.array([True, True, False])
             tm.assert_numpy_array_equal(result, expected)
 
             expected = np.array([False, False, False])
-            tm.assert_numpy_array_equal(l == pd.NaT, expected)
-            tm.assert_numpy_array_equal(pd.NaT == r, expected)
+            tm.assert_numpy_array_equal(lhs == pd.NaT, expected)
+            tm.assert_numpy_array_equal(pd.NaT == rhs, expected)
 
             expected = np.array([True, True, True])
-            tm.assert_numpy_array_equal(l != pd.NaT, expected)
-            tm.assert_numpy_array_equal(pd.NaT != l, expected)
+            tm.assert_numpy_array_equal(lhs != pd.NaT, expected)
+            tm.assert_numpy_array_equal(pd.NaT != lhs, expected)
 
             expected = np.array([False, False, False])
-            tm.assert_numpy_array_equal(l < pd.NaT, expected)
-            tm.assert_numpy_array_equal(pd.NaT > l, expected)
+            tm.assert_numpy_array_equal(lhs < pd.NaT, expected)
+            tm.assert_numpy_array_equal(pd.NaT > lhs, expected)
 
     def test_value_counts_unique(self):
         # GH 7735