diff --git a/doc/source/v0.15.0.txt b/doc/source/v0.15.0.txt
index 7e0931ca1b745..39ff807d6b1e4 100644
--- a/doc/source/v0.15.0.txt
+++ b/doc/source/v0.15.0.txt
@@ -97,7 +97,7 @@ Categoricals in Series/DataFrame
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 :class:`~pandas.Categorical` can now be included in `Series` and `DataFrames` and gained new
-methods to manipulate. Thanks to Jan Schultz for much of this API/implementation. (:issue:`3943`, :issue:`5313`, :issue:`5314`, :issue:`7444`).
+methods to manipulate. Thanks to Jan Schultz for much of this API/implementation. (:issue:`3943`, :issue:`5313`, :issue:`5314`, :issue:`7444`, :issue:`7839`).
 
 For full docs, see the :ref:`Categorical introduction <categorical>` and the :ref:`API documentation <api.categorical>`.
 
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 502c01ce6d1d1..c0e1e8a13eea3 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -973,7 +973,9 @@ def _get_repr(
         return result
 
     def __iter__(self):
-        if np.issubdtype(self.dtype, np.datetime64):
+        if  com.is_categorical_dtype(self.dtype):
+            return iter(self.values)
+        elif np.issubdtype(self.dtype, np.datetime64):
             return (lib.Timestamp(x) for x in self.values)
         else:
             return iter(self.values)
diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py
index 0aa7f2b67c7c6..b70e50eb3d030 100644
--- a/pandas/tests/test_categorical.py
+++ b/pandas/tests/test_categorical.py
@@ -647,6 +647,27 @@ def test_nan_handling(self):
                                       np.array(["a","b",np.nan], dtype=np.object_))
         self.assert_numpy_array_equal(s3.cat._codes, np.array([0,1,2,0]))
 
+    def test_sequence_like(self):
+
+        # GH 7839
+        # make sure can iterate
+        df = DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})
+        df['grade'] = Categorical(df['raw_grade'])
+
+        # basic sequencing testing
+        result = list(df.grade.cat)
+        expected = np.array(df.grade.cat).tolist()
+        tm.assert_almost_equal(result,expected)
+
+        # iteration
+        for t in df.itertuples(index=False):
+            str(t)
+
+        for row, s in df.iterrows():
+            str(s)
+
+        for c, col in df.iteritems():
+            str(s)
 
     def test_series_delegations(self):