Merge pull request #3145 from jreback/transform

PERF: GH2121 groupby transform

RELEASE.rst

@@ -100,9 +100,6 @@ pandas 0.11.0
     the collections.Mapping ABC.
   - Allow selection semantics via a string with a datelike index to work in both
     Series and DataFrames (GH3070_)
-  - Improved performance across several core functions by taking memory
-    ordering of arrays into account. Courtesy of @stephenwlin (GH3130_)
-
 
     .. ipython:: python
 
@@ -116,6 +113,10 @@ pandas 0.11.0
     for plots. Based on https://gist.github.com/huyng/816622 (GH3075_).
 
 
+  - Improved performance across several core functions by taking memory
+    ordering of arrays into account. Courtesy of @stephenwlin (GH3130_)
+  - Improved performance of groupby transform method (GH2121_)
+
 **API Changes**
 
   - Do not automatically upcast numeric specified dtypes to ``int64`` or
@@ -234,6 +235,7 @@ pandas 0.11.0
 .. _GH622: https://github.com/pydata/pandas/issues/622
 .. _GH797: https://github.com/pydata/pandas/issues/797
 .. _GH2758: https://github.com/pydata/pandas/issues/2758
+.. _GH2121: https://github.com/pydata/pandas/issues/2121
 .. _GH2809: https://github.com/pydata/pandas/issues/2809
 .. _GH2810: https://github.com/pydata/pandas/issues/2810
 .. _GH2837: https://github.com/pydata/pandas/issues/2837

pandas/core/groupby.py

@@ -13,7 +13,7 @@
 from pandas.util.compat import OrderedDict
 import pandas.core.algorithms as algos
 import pandas.core.common as com
-from pandas.core.common import _possibly_downcast_to_dtype
+from pandas.core.common import _possibly_downcast_to_dtype, notnull
 
 import pandas.lib as lib
 import pandas.algos as _algos
@@ -75,7 +75,7 @@ def f(self):
 def _first_compat(x, axis=0):
     def _first(x):
         x = np.asarray(x)
-        x = x[com.notnull(x)]
+        x = x[notnull(x)]
         if len(x) == 0:
             return np.nan
         return x[0]
@@ -89,7 +89,7 @@ def _first(x):
 def _last_compat(x, axis=0):
     def _last(x):
         x = np.asarray(x)
-        x = x[com.notnull(x)]
+        x = x[notnull(x)]
         if len(x) == 0:
             return np.nan
         return x[-1]
@@ -421,7 +421,7 @@ def ohlc(self):
 
     def nth(self, n):
         def picker(arr):
-            arr = arr[com.notnull(arr)]
+            arr = arr[notnull(arr)]
             if len(arr) >= n + 1:
                 return arr.iget(n)
             else:
@@ -1897,19 +1897,46 @@ def transform(self, func, *args, **kwargs):
         gen = self.grouper.get_iterator(obj, axis=self.axis)
 
         if isinstance(func, basestring):
-            wrapper = lambda x: getattr(x, func)(*args, **kwargs)
+            fast_path = lambda group: getattr(group, func)(*args, **kwargs)
+            slow_path = lambda group: group.apply(lambda x: getattr(x, func)(*args, **kwargs), axis=self.axis)
         else:
-            wrapper = lambda x: func(x, *args, **kwargs)
+            fast_path = lambda group: func(group, *args, **kwargs)
+            slow_path = lambda group: group.apply(lambda x: func(x, *args, **kwargs), axis=self.axis)
 
+        path = None
         for name, group in gen:
             object.__setattr__(group, 'name', name)
 
-            try:
-                res = group.apply(wrapper, axis=self.axis)
-            except TypeError:
-                return self._transform_item_by_item(obj, wrapper)
-            except Exception:  # pragma: no cover
-                res = wrapper(group)
+            # decide on a fast path
+            if path is None:
+
+                path = slow_path
+                try:
+                    res  = slow_path(group)
+
+                    # if we make it here, test if we can use the fast path
+                    try:
+                        res_fast = fast_path(group)
+
+                        # compare that we get the same results
+                        if res.shape == res_fast.shape:
+                            res_r = res.values.ravel()
+                            res_fast_r = res_fast.values.ravel()
+                            mask = notnull(res_r)
+                            if (res_r[mask] == res_fast_r[mask]).all():
+                                path = fast_path
+
+                    except:
+                        pass
+                except TypeError:
+                    return self._transform_item_by_item(obj, fast_path)
+                except Exception:  # pragma: no cover
+                    res  = fast_path(group)
+                    path = fast_path
+
+            else:
+
+                res = path(group)
 
             # broadcasting
             if isinstance(res, Series):
@@ -1925,7 +1952,8 @@ def transform(self, func, *args, **kwargs):
         concat_index = obj.columns if self.axis == 0 else obj.index
         concatenated = concat(applied, join_axes=[concat_index],
                               axis=self.axis, verify_integrity=False)
-        return concatenated.reindex_like(obj)
+        concatenated.sort_index(inplace=True)
+        return concatenated
 
     def _transform_item_by_item(self, obj, wrapper):
         # iterate through columns

vb_suite/groupby.py

@@ -273,3 +273,41 @@ def f(g):
 """
 
 groupby_sum_booleans = Benchmark("df.groupby('ii').sum()", setup)
+
+#----------------------------------------------------------------------
+# Transform testing
+
+setup = common_setup + """
+n_dates = 1000
+n_securities = 500
+n_columns = 3
+share_na = 0.1
+
+dates = date_range('1997-12-31', periods=n_dates, freq='B')
+dates = Index(map(lambda x: x.year * 10000 + x.month * 100 + x.day, dates))
+
+secid_min = int('10000000', 16)
+secid_max = int('F0000000', 16)
+step = (secid_max - secid_min) // (n_securities - 1)
+security_ids = map(lambda x: hex(x)[2:10].upper(), range(secid_min, secid_max + 1, step))
+
+data_index = MultiIndex(levels=[dates.values, security_ids],
+    labels=[[i for i in xrange(n_dates) for _ in xrange(n_securities)], range(n_securities) * n_dates],
+    names=['date', 'security_id'])
+n_data = len(data_index)
+
+columns = Index(['factor{}'.format(i) for i in xrange(1, n_columns + 1)])
+
+data = DataFrame(np.random.randn(n_data, n_columns), index=data_index, columns=columns)
+
+step = int(n_data * share_na)
+for column_index in xrange(n_columns):
+    index = column_index
+    while index < n_data:
+        data.set_value(data_index[index], columns[column_index], np.nan)
+        index += step
+
+f_fillna = lambda x: x.fillna(method='pad')
+"""
+
+groupby_transform = Benchmark("data.groupby(level='security_id').transform(f_fillna)", setup)