diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py
index 6179857978b7b..f529af6543939 100644
--- a/pandas/core/groupby.py
+++ b/pandas/core/groupby.py
@@ -76,7 +76,7 @@
     'cumsum', 'cumprod', 'cummin', 'cummax', 'cumcount',
     'resample',
     'describe',
-    'rank', 'quantile',
+    'quantile',
     'fillna',
     'mad',
     'any', 'all',
@@ -1378,6 +1378,32 @@ def cumsum(self, axis=0, *args, **kwargs):
 
         return self._cython_transform('cumsum')
 
+    @Substitution(name='groupby')
+    @Appender(_doc_template)
+    def rank(self, axis=0, method='average', numeric_only=True,
+             na_option='keep', ascending=True, pct=False):
+        """Compute numerical data ranks (1 through n) along axis.
+        """
+
+        def wrapper(values):
+            return values.rank(axis=axis, method=method, na_option=na_option,
+                               ascending=ascending, pct=pct)
+
+        try:
+            return self.transform(wrapper)
+        except ValueError:
+            if not numeric_only and method == 'first':
+                raise ValueError('first not supported for non-numeric data')
+                # such a ValueError is raised by pandas.algos.rank_2d_generic
+                # for regular (non-grouped) dataframes
+            if numeric_only:
+                data = self._obj_with_exclusions._get_numeric_data()
+                if data.size == 0:
+                    raise DataError('No numeric types to aggregate')
+                data = data.groupby(self.grouper)
+                return data.transform(wrapper)
+            raise
+
     @Substitution(name='groupby')
     @Appender(_doc_template)
     def shift(self, periods=1, freq=None, axis=0):
@@ -3182,6 +3208,7 @@ def aggregate(self, arg, *args, **kwargs):
     agg = aggregate
 
     def _aggregate_generic(self, func, *args, **kwargs):
+
         if self.grouper.nkeys != 1:
             raise AssertionError('Number of keys must be 1')
 
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py
index 3f5b4152afe31..ba0b343aa49ce 100644
--- a/pandas/tests/test_groupby.py
+++ b/pandas/tests/test_groupby.py
@@ -3646,6 +3646,92 @@ def test_column_select_via_attr(self):
         expected = self.df.groupby('A').agg(np.mean)
         assert_frame_equal(result, expected)
 
+    def test_rank(self):
+        # normal behavior
+        df = DataFrame({'a': ['A1', 'A1', 'A1'],
+                        'b': [2, 1, 1],
+                        'c': 1.})
+        df = df.set_index('a')
+        dg = df.groupby('c')
+        expected = DataFrame({'a': ['A1', 'A1', 'A1'],
+                              'b': [3., 1., 2.]})
+        expected = expected.set_index('a')
+
+        result = dg.rank(method='first')
+        assert_frame_equal(result, expected)
+
+        result = dg.rank(method='first', numeric_only=True)
+        assert_frame_equal(result, expected)
+
+        result = dg.rank(method='first', numeric_only=False)
+        assert_frame_equal(result, expected)
+
+        # GH 11759: non numeric data
+        df = DataFrame({'a': ['A1', 'A1', 'A1'],
+                        'b': ['B2', 'B1', 'B1'],
+                        'c': 1.})
+        df = df.set_index('a')
+        dg = df.groupby('c')
+        self.assertRaises(DataError, dg.rank,
+                          method='first')
+        self.assertRaises(DataError, dg.rank,
+                          method='first', numeric_only=True)
+        self.assertRaises(ValueError, dg.rank,
+                          method='first', numeric_only=False)
+        # such a ValueError is raised by pandas.algos.rank_2d_generic
+        # for regular (non-grouped) dataframes
+
+        # with categorical data
+        df = DataFrame({'a': ['A1', 'A1', 'A1'],
+                        'b': Categorical(['big', 'small', 'small'],
+                                         categories=['small', 'big'],
+                                         ordered=True),
+                        'c': 1.})
+        df = df.set_index('a')
+        dg = df.groupby('c')
+        self.assertRaises(DataError, dg.rank,
+                          method='first')
+        self.assertRaises(DataError, dg.rank,
+                          method='first', numeric_only=True)
+        self.assertRaises(ValueError, dg.rank,
+                          method='first', numeric_only=False)
+
+        # with datetime data
+        df = DataFrame({'a': ['A1', 'A1', 'A1'],
+                        'b': [datetime(2002, 2, 2), datetime(2001, 1, 1),
+                              datetime(2001, 1, 1)],
+                        'c': 1.})
+        df = df.set_index('a')
+        dg = df.groupby('c')
+
+        result = dg.rank(method='first')
+        assert_frame_equal(result, expected)
+
+        result = dg.rank(method='first', numeric_only=True)
+        assert_frame_equal(result, expected)
+
+        result = dg.rank(method='first', numeric_only=False)
+        assert_frame_equal(result, expected)
+
+        # with another numeric column
+        df = DataFrame({'a': ['A1', 'A1', 'A1'],
+                        'b': ['B2', 'B1', 'B1'],
+                        'c': 1.,
+                        'd': 1.})
+        df = df.set_index('a')
+        dg = df.groupby('c')
+        expected = df.drop('b', axis=1).groupby('c').rank(method='first')
+
+        result = dg.rank(method='first')
+        assert_frame_equal(result, expected)
+
+        result = dg.rank(method='first', numeric_only=True)
+        assert_frame_equal(result, expected)
+
+        self.assertRaises(ValueError, dg.rank,
+                          method='first', numeric_only=False)
+        # same remark as above
+
     def test_rank_apply(self):
         lev1 = tm.rands_array(10, 100)
         lev2 = tm.rands_array(10, 130)
@@ -5753,7 +5839,6 @@ def test_groupby_whitelist(self):
             'cumcount',
             'resample',
             'describe',
-            'rank',
             'quantile',
             'fillna',
             'mad',
@@ -5794,7 +5879,6 @@ def test_groupby_whitelist(self):
             'cumcount',
             'resample',
             'describe',
-            'rank',
             'quantile',
             'fillna',
             'mad',