Removed vectorization from groupby pct change

Author: simonariddell

pandas/core/groupby/groupby.py

@@ -2070,17 +2070,10 @@ def shift(self, periods=1, freq=None, axis=0):
     def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None,
                    axis=0):
         """Calcuate pct_change of each value to previous entry in group"""
-        if (freq is not None or axis != 0) or not self.grouper.is_monotonic:
-            return self.apply(lambda x: x.pct_change(periods=periods,
-                                                     fill_method=fill_method,
-                                                     limit=limit, freq=freq,
-                                                     axis=axis))
-
-        filled = getattr(self, fill_method)(limit=limit).drop(
-            self.grouper.names, axis=1)
-        shifted = filled.shift(periods=periods, freq=freq)
-
-        return (filled / shifted) - 1
+        return self.apply(lambda x: x.pct_change(periods=periods,
+                                                 fill_method=fill_method,
+                                                 limit=limit, freq=freq,
+                                                 axis=axis))
 
     @Substitution(name='groupby')
     @Appender(_doc_template)
@@ -3943,15 +3936,9 @@ def _apply_to_column_groupbys(self, func):
 
     def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None):
         """Calcuate pct_change of each value to previous entry in group"""
-        if not self.grouper.is_monotonic:
-            return self.apply(lambda x: x.pct_change(periods=periods,
-                                                     fill_method=fill_method,
-                                                     limit=limit, freq=freq))
-
-        filled = getattr(self, fill_method)(limit=limit)
-        shifted = filled.shift(periods=periods, freq=freq)
-
-        return (filled / shifted) - 1
+        return self.apply(lambda x: x.pct_change(periods=periods,
+                                                 fill_method=fill_method,
+                                                 limit=limit, freq=freq))
 
 
 class NDFrameGroupBy(GroupBy):

pandas/tests/groupby/test_transform.py

@@ -729,15 +729,13 @@ def interweave(list_obj):
     (-1, 'ffill', None), (-1, 'ffill', 1),
     (-1, 'bfill', None), (-1, 'bfill', 1)])
 def test_pct_change(test_series, shuffle, periods, fill_method, limit):
-    # Groupby pct change uses an apply if monotonic
-    # and a vectorized operation if non-monotonic
-    # Shuffle parameter tests each
-    vals = [np.nan, np.nan, 1, 2, 4, 10, np.nan, np.nan]
+    vals = [3, np.nan, 1, 2, 4, 10, np.nan, np.nan]
     keys = ['a', 'b']
     key_v = [k for j in list(map(lambda x: [x] * len(vals), keys)) for k in j]
     df = DataFrame({'key': key_v, 'vals': vals * 2})
     if shuffle:
-        df = df.reindex(np.random.permutation(len(df))).reset_index(drop=True)
+        order = np.random.RandomState(seed=42).permutation(len(df))
+        df = df.reindex(order).reset_index(drop=True)
 
     manual_apply = []
     for k in keys:
@@ -746,30 +744,27 @@ def test_pct_change(test_series, shuffle, periods, fill_method, limit):
                                                 fill_method=fill_method,
                                                 limit=limit))
     exp_vals = pd.concat(manual_apply).reset_index(drop=True)
-    exp = pd.DataFrame(exp_vals, columns=['_pct_change'])
+    exp = pd.DataFrame(exp_vals, columns=['A'])
     grp = df.groupby('key')
 
     def get_result(grp_obj):
         return grp_obj.pct_change(periods=periods,
                                   fill_method=fill_method,
                                   limit=limit)
 
-    # Specifically test when monotonic and not monotonic
-
     if test_series:
-        exp = exp.loc[:, '_pct_change']
+        exp = exp.loc[:, 'A']
         grp = grp['vals']
         result = get_result(grp)
-        # Resort order by keys to compare to expected values
-        df.insert(0, '_pct_change', result)
+        df.insert(0, 'A', result)
         result = df.sort_values(by='key')
-        result = result.loc[:, '_pct_change']
+        result = result.loc[:, 'A']
         result = result.reset_index(drop=True)
         tm.assert_series_equal(result, exp)
     else:
         result = get_result(grp)
         result.reset_index(drop=True, inplace=True)
-        result.columns = ['_pct_change']
+        result.columns = ['A']
         tm.assert_frame_equal(result, exp)