diff --git a/contrib/connectors/pandas/models.py b/contrib/connectors/pandas/models.py
index 816b360ba7c6f..b7645d1d4d568 100644
--- a/contrib/connectors/pandas/models.py
+++ b/contrib/connectors/pandas/models.py
@@ -98,13 +98,6 @@ def is_dttm(self):
     def is_string(self):
         return self.type and is_string_dtype(self.type)
 
-    num_types = (
-        'DOUBLE', 'FLOAT', 'INT', 'BIGINT',
-        'LONG', 'REAL', 'NUMERIC', 'DECIMAL'
-    )
-    date_types = ('DATE', 'TIME', 'DATETIME')
-    str_types = ('VARCHAR', 'STRING', 'CHAR')
-
     @property
     def data(self):
         attrs = (
@@ -366,8 +359,8 @@ def get_filter_query(self, filter):
                 # Rely on Pandas partial string indexing for datetime fields,
                 # see https://pandas.pydata.org/pandas-docs/stable/timeseries.html#partial-string-indexing  # NOQA
                 try:
-                    if ((col_obj.is_string or col_obj.is_dttm)
-                            and not isinstance(eq, list)):
+                    if ((col_obj.is_string or col_obj.is_dttm) and
+                            not isinstance(eq, list)):
                         eq = "'{}'".format(eq)
                 except AttributeError:
                     # col_obj is None, probably because the col is a metric,
@@ -472,12 +465,13 @@ def process_dataframe(
                     metric = metrics_dict[timeseries_limit_metric]
                     assert isinstance(metric.source, basestring)
                     aggregates = {metric.source: metric.expression}
-                    df = (df[df.set_index(groupby).index.isin(
-                              df.groupby(groupby, sort=False)
-                                .aggregate(aggregates)
-                                .sort_values(metric.source,
-                                             ascending=metric_order_asc)
-                                .iloc[:timeseries_limit].index)])
+                    df = (df[df.set_index(groupby)
+                               .index.isin(
+                                   df.groupby(groupby, sort=False)
+                                   .aggregate(aggregates)
+                                   .sort_values(metric.source,
+                                                ascending=metric_order_asc)
+                                   .iloc[:timeseries_limit].index)])
 
                     query_str += ('[df.set_index({groupby}).index.isin('
                                   'df.groupby({groupby}, sort=False)'
diff --git a/contrib/tests/connector_tests.py b/contrib/tests/connector_tests.py
index 632bb829ea681..631f0c11da5db 100644
--- a/contrib/tests/connector_tests.py
+++ b/contrib/tests/connector_tests.py
@@ -654,7 +654,8 @@ def test_groupby_value_percentage_metric(self):
         self.assertEqual(result.error_message, None)
         self.assertEqual(result.status, QueryStatus.SUCCESS)
         expected_df = (self.df.groupby(parameters['groupby'])
-                              .apply(lambda x: sum(x['value'])/sum(x['value'] + x['value2']))
+                              .apply(lambda x: sum(x['value']) /
+                                     sum(x['value'] + x['value2']))
                               .reset_index()
                               .sort_values([0], ascending=False))
         expected_df.columns = parameters['groupby'] + parameters['metrics']
@@ -935,7 +936,8 @@ class SqlaConnectorTestCase(BaseConnectorTestCase):
         SqlMetric(metric_name='value_percentage', metric_type='custom',
                   expression="SUM(value)/SUM(value + value2)"),
         SqlMetric(metric_name='category_percentage', metric_type='custom',
-                  expression="SUM(CASE WHEN category='CategoryA' THEN 1 ELSE 0 END)/CAST(COUNT(*) AS REAL)"),
+                  expression="SUM(CASE WHEN category='CategoryA' THEN 1 ELSE 0 END)/"
+                             "CAST(COUNT(*) AS REAL)"),
     ]
 
     def setUp(self):
@@ -993,7 +995,7 @@ def setUp(self):
                                            metrics=self.metrics)
 
         def calc_value_percentage(group):
-            return sum(group['value'])/sum(group['value'] + group['value2'])
+            return sum(group['value']) / sum(group['value'] + group['value2'])
 
         self.datasource.calc_value_percentage = calc_value_percentage