refactor: flake8

Author: keyanyang

mlxtend/frequent_patterns/apriori.py

@@ -324,8 +324,8 @@ def _support(_x, _n_rows, _is_sparse):
     res_df.columns = ['support', 'itemsets']
     if use_colnames:
         mapping = {idx: item for idx, item in enumerate(df.columns)}
-        res_df['itemsets'] = [frozenset(a) for a in np.vectorize(map)\
-            (mapping.get, res_df['itemsets'])]
+        res_df['itemsets'] = [frozenset(a) for a in np.vectorize(map)(
+            mapping.get, res_df['itemsets'])]
     res_df = res_df.reset_index(drop=True)
 
     if verbose:

mlxtend/frequent_patterns/fpcommon.py

@@ -70,8 +70,8 @@ def generate_itemsets(generator, num_itemsets, colname_map):
     res_df = pd.DataFrame({'support': supports, 'itemsets': itemsets})
 
     if colname_map is not None:
-        res_df['itemsets'] = [frozenset(a) for a in np.vectorize(map)\
-            (colname_map.get, res_df['itemsets'])]
+        res_df['itemsets'] = [frozenset(a) for a in np.vectorize(map)(
+            colname_map.get, res_df['itemsets'])]
     return res_df
 
 

mlxtend/frequent_patterns/tests/test_association_rules.py

@@ -44,10 +44,10 @@ def test_default():
         columns=columns_ordered
     )
 
-    expect['antecedents'] = np.vectorize(str)\
-        (np.vectorize(frozenset)(expect['antecedents']))
-    expect['consequents'] = np.vectorize(str)\
-        (np.vectorize(frozenset)(expect['consequents']))
+    expect['antecedents'] = np.vectorize(str)(
+        np.vectorize(frozenset)(expect['antecedents']))
+    expect['consequents'] = np.vectorize(str)(
+        np.vectorize(frozenset)(expect['consequents']))
     expect.sort_values(columns_ordered, inplace=True)
     expect.reset_index(inplace=True, drop=True)
 
@@ -66,8 +66,8 @@ def test_datatypes():
     # check if association_rule converts it internally
     # back to frozensets
     df_freq_items_copy = df_freq_items.copy()
-    df_freq_items_copy['itemsets'] = np.vectorize(set)\
-        (df_freq_items_copy['itemsets'])
+    df_freq_items_copy['itemsets'] = np.vectorize(set)(
+        df_freq_items_copy['itemsets'])
 
     res_df = association_rules(df_freq_items)
     for i in res_df['antecedents']: