diff --git a/pandas/core/series.py b/pandas/core/series.py
index c0a013e387a04..30e91ecf717d4 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -1345,22 +1345,25 @@ def describe(self, percentile_width=50):
             from pandas.util.counter import Counter
 
         if self.dtype == object:
-            names = ['count', 'unique', 'top', 'freq']
-
+            names = ['count', 'unique']
             objcounts = Counter(self.dropna().values)
-            top, freq = objcounts.most_common(1)[0]
-            data = [self.count(), len(objcounts), top, freq]
+            data = [self.count(), len(objcounts)]
+            if data[1] > 0:
+                names += ['top', 'freq']
+                top, freq = objcounts.most_common(1)[0]
+                data += [top, freq]
 
         elif issubclass(self.dtype.type, np.datetime64):
-            names = ['count', 'unique', 'first', 'last', 'top', 'freq']
-
+            names = ['count', 'unique']
             asint = self.dropna().view('i8')
             objcounts = Counter(asint)
-            top, freq = objcounts.most_common(1)[0]
-            data = [self.count(), len(objcounts),
-                    lib.Timestamp(asint.min()),
-                    lib.Timestamp(asint.max()),
-                    lib.Timestamp(top), freq]
+            data = [self.count(), len(objcounts)]
+            if data[1] > 0:
+                top, freq = objcounts.most_common(1)[0]
+                names += ['first', 'last', 'top', 'freq']
+                data += [lib.Timestamp(asint.min()),
+                         lib.Timestamp(asint.max()),
+                         lib.Timestamp(top), freq]
         else:
 
             lb = .5 * (1. - percentile_width/100.)
@@ -1373,13 +1376,14 @@ def pretty_name(x):
                 else:
                     return '%.1f%%' % x
 
-            names = ['count', 'mean', 'std', 'min',
-                     pretty_name(lb), '50%', pretty_name(ub),
-                     'max']
-
-            data = [self.count(), self.mean(), self.std(), self.min(),
-                    self.quantile(lb), self.median(), self.quantile(ub),
-                    self.max()]
+            names = ['count']
+            data = [self.count()]
+            if data[0] > 0:
+                names += ['mean', 'std', 'min', pretty_name(lb), '50%',
+                        pretty_name(ub), 'max']
+                data += [self.mean(), self.std(), self.min(),
+                        self.quantile(lb), self.median(), self.quantile(ub),
+                        self.max()]
 
         return Series(data, index=names)
 
diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py
index 5ad88a67c1042..667072d0453ab 100644
--- a/pandas/tests/test_series.py
+++ b/pandas/tests/test_series.py
@@ -210,6 +210,12 @@ def setUp(self):
 
         self.empty = Series([], index=[])
 
+        self.noneSeries = Series([None])
+        self.noneSeries.name = 'None'
+
+        self.nanSeries = Series([np.nan])
+        self.nanSeries.name = 'NaN'
+
     def test_constructor(self):
         # Recognize TimeSeries
         self.assert_(isinstance(self.ts, TimeSeries))
@@ -1249,6 +1255,18 @@ def test_describe_objects(self):
                      'top' : min_date}, index=rs.index)
         assert_series_equal(rs, xp)
 
+    def test_describe_empty(self):
+        assert_series_equal(self.empty.describe(),
+                            Series([0], index=['count']))
+
+    def test_describe_none(self):
+        assert_series_equal(self.noneSeries.describe(),
+                            Series([0, 0], index=['count', 'unique']))
+
+    def test_describe_nan(self):
+        assert_series_equal(self.nanSeries.describe(), 
+                            Series([0], index=['count']))
+
     def test_append(self):
         appendedSeries = self.series.append(self.objSeries)
         for idx, value in appendedSeries.iteritems():