diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py
index fff28c329494b..5a0bc590eb4f4 100644
--- a/pandas/core/groupby.py
+++ b/pandas/core/groupby.py
@@ -3568,7 +3568,8 @@ def first_non_None_value(values):
                 # as we are stacking can easily have object dtypes here
                 so = self._selected_obj
                 if (so.ndim == 2 and so.dtypes.apply(is_datetimelike).any()):
-                    result = result.apply(lambda x: pd.to_numeric(x, errors='ignore'))
+                    result = result.apply(
+                        lambda x: pd.to_numeric(x, errors='ignore'))
                     date_cols = self._selected_obj.select_dtypes(
                         include=['datetime', 'timedelta']).columns
                     date_cols = date_cols.intersection(result.columns)
diff --git a/pandas/tests/groupby/test_groupby.py b/pandas/tests/groupby/test_groupby.py
index fb0a103caef59..263eb6497e990 100644
--- a/pandas/tests/groupby/test_groupby.py
+++ b/pandas/tests/groupby/test_groupby.py
@@ -4316,12 +4316,15 @@ def test_cummin_cummax(self):
 
     def test_numeric_coercion(self):
         # GH 14423
-        df = pd.DataFrame({'Number' : [1, 2], 'Date' : ["2017-03-02"] * 2, 'Str' : ["foo", "inf"]})
+        df = pd.DataFrame({'Number': [1, 2],
+                           'Date': ["2017-03-02"] * 2,
+                           'Str': ["foo", "inf"]})
         expected = df.groupby(['Number']).apply(lambda x: x.iloc[0])
         df.Date = pd.to_datetime(df.Date)
         result = df.groupby(['Number']).apply(lambda x: x.iloc[0])
         tm.assert_series_equal(result['Str'], expected['Str'])
-        
+
+
 def _check_groupby(df, result, keys, field, f=lambda x: x.sum()):
     tups = lmap(tuple, df[keys].values)
     tups = com._asarray_tuplesafe(tups)