diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 5e617671f5c49..a031b0550c734 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -4575,7 +4575,7 @@ def extract_index(data):
 
 
 def _prep_ndarray(values, copy=True):
-    if not isinstance(values, np.ndarray):
+    if not isinstance(values, (np.ndarray,Series)):
         if len(values) == 0:
             return np.empty((0, 0), dtype=object)
 
diff --git a/vb_suite/frame_ctor.py b/vb_suite/frame_ctor.py
index f8d6e5d548ae5..1d8df95de9fe3 100644
--- a/vb_suite/frame_ctor.py
+++ b/vb_suite/frame_ctor.py
@@ -25,19 +25,24 @@
 frame_ctor_nested_dict = Benchmark("DataFrame(data)", setup)
 
 # From JSON-like stuff
-
 frame_ctor_list_of_dict = Benchmark("DataFrame(dict_list)", setup,
                                     start_date=datetime(2011, 12, 20))
 
 series_ctor_from_dict = Benchmark("Series(some_dict)", setup)
 
 # nested dict, integer indexes, regression described in #621
-
 setup = common_setup + """
 data = dict((i,dict((j,float(j)) for j in xrange(100))) for i in xrange(2000))
 """
 frame_ctor_nested_dict_int64 = Benchmark("DataFrame(data)", setup)
 
+# from a mi-series
+setup = common_setup + """
+mi = MultiIndex.from_tuples([(x,y) for x in range(100) for y in range(100)])
+s = Series(randn(10000), index=mi)
+"""
+frame_from_series = Benchmark("DataFrame(s)", setup)
+
 #----------------------------------------------------------------------
 # get_numeric_data