broadcast always returns original column names

Author: jreback

pandas/core/apply.py

@@ -187,7 +187,6 @@ def apply_broadcast(self, target):
  # axis which we want to compare compliance
  result_compare = target.shape[0]
 
- index = None
  for i, col in enumerate(target.columns):
  res = self.f(target[col])
  ares = np. asarray(res).ndim
@@ -201,19 +200,11 @@ def apply_broadcast(self, target):
  if result_compare != len(res):
  raise ValueError("cannot broadcast result")
 
- # if we have a Series result, then then index
- # is our result
- if isinstance(res, ABCSeries):
- index = res.index
-
  result_values[:, i] = res
 
- # if we are returning a list-like
- # then preserve the original index
- if index is None:
- index = target.index
-
- result = self.obj._constructor(result_values, index=index,
+ # we *always* preserve the original index / columns
+ result = self.obj._constructor(result_values,
+ index=target.index,
  columns=target.columns)
  return result
 

pandas/core/frame.py

@@ -4877,10 +4877,11 @@ def apply(self, func, axis=0, broadcast=None, raw=False, reduce=None,
 
  result_type : {'expand', 'reduce', 'broadcast, None}
  These only act when axis=1 {columns}
- * 'expand' : list-like results will be turned into columns
+ * 'expand' : list-like results will be turned into columns.
  * 'reduce' : return a Series if possible rather than expanding
- list-like results. This is the opposite to 'expand'
- * 'broadcast' : scalar results will be broadcast to all columns
+ list-like results. This is the opposite to 'expand'.
+ * 'broadcast' : results will be broadcast to the original shape
+ of the frame, the original index & columns will be retained.
  * None : list-like results will be returned as a list
  in a single column. However if the apply function
  returns a Series these are expanded to columns.

pandas/tests/frame/test_apply.py

@@ -171,14 +171,12 @@ def test_apply_broadcast(self):
  result_type='broadcast')
  tm.assert_frame_equal(result, df)
 
- # columms come from the returned Series
  df = DataFrame(np.tile(np.arange(3), 6).reshape(6, -1) + 1,
  columns=list('ABC'))
  result = df.apply(lambda x: Series([1, 2, 3], index=list('abc')),
  axis=1,
  result_type='broadcast')
  expected = df.copy()
- expected.columns = list('abc')
  tm.assert_frame_equal(result, expected)
 
  def test_apply_broadcast_error(self):
@@ -756,7 +754,6 @@ def test_result_type(self):
  axis=1,
  result_type='broadcast')
  expected = df.copy()
- expected.columns = columns
  assert_frame_equal(result, expected)
 
  # series result