diff --git a/pandas/tests/frame/test_rank.py b/pandas/tests/frame/test_rank.py
index eed91850888fc..1e600ea8cd878 100644
--- a/pandas/tests/frame/test_rank.py
+++ b/pandas/tests/frame/test_rank.py
@@ -29,19 +29,19 @@ def method(self, request):
         """
         return request.param
 
-    def test_rank(self):
+    def test_rank(self, float_frame):
         rankdata = pytest.importorskip('scipy.stats.rankdata')
 
-        self.frame['A'][::2] = np.nan
-        self.frame['B'][::3] = np.nan
-        self.frame['C'][::4] = np.nan
-        self.frame['D'][::5] = np.nan
+        float_frame['A'][::2] = np.nan
+        float_frame['B'][::3] = np.nan
+        float_frame['C'][::4] = np.nan
+        float_frame['D'][::5] = np.nan
 
-        ranks0 = self.frame.rank()
-        ranks1 = self.frame.rank(1)
-        mask = np.isnan(self.frame.values)
+        ranks0 = float_frame.rank()
+        ranks1 = float_frame.rank(1)
+        mask = np.isnan(float_frame.values)
 
-        fvals = self.frame.fillna(np.inf).values
+        fvals = float_frame.fillna(np.inf).values
 
         exp0 = np.apply_along_axis(rankdata, 0, fvals)
         exp0[mask] = np.nan
@@ -109,32 +109,32 @@ def test_rank2(self):
         result = df.rank(1, numeric_only=False, ascending=False)
         tm.assert_frame_equal(result, expected)
 
-        # mixed-type frames
-        self.mixed_frame['datetime'] = datetime.now()
-        self.mixed_frame['timedelta'] = timedelta(days=1, seconds=1)
-
-        result = self.mixed_frame.rank(1)
-        expected = self.mixed_frame.rank(1, numeric_only=True)
-        tm.assert_frame_equal(result, expected)
-
         df = DataFrame({"a": [1e-20, -5, 1e-20 + 1e-40, 10,
                               1e60, 1e80, 1e-30]})
         exp = DataFrame({"a": [3.5, 1., 3.5, 5., 6., 7., 2.]})
         tm.assert_frame_equal(df.rank(), exp)
 
-    def test_rank_na_option(self):
+    def test_rank_mixed_frame(self, float_string_frame):
+        float_string_frame['datetime'] = datetime.now()
+        float_string_frame['timedelta'] = timedelta(days=1, seconds=1)
+
+        result = float_string_frame.rank(1)
+        expected = float_string_frame.rank(1, numeric_only=True)
+        tm.assert_frame_equal(result, expected)
+
+    def test_rank_na_option(self, float_frame):
         rankdata = pytest.importorskip('scipy.stats.rankdata')
 
-        self.frame['A'][::2] = np.nan
-        self.frame['B'][::3] = np.nan
-        self.frame['C'][::4] = np.nan
-        self.frame['D'][::5] = np.nan
+        float_frame['A'][::2] = np.nan
+        float_frame['B'][::3] = np.nan
+        float_frame['C'][::4] = np.nan
+        float_frame['D'][::5] = np.nan
 
         # bottom
-        ranks0 = self.frame.rank(na_option='bottom')
-        ranks1 = self.frame.rank(1, na_option='bottom')
+        ranks0 = float_frame.rank(na_option='bottom')
+        ranks1 = float_frame.rank(1, na_option='bottom')
 
-        fvals = self.frame.fillna(np.inf).values
+        fvals = float_frame.fillna(np.inf).values
 
         exp0 = np.apply_along_axis(rankdata, 0, fvals)
         exp1 = np.apply_along_axis(rankdata, 1, fvals)
@@ -143,11 +143,11 @@ def test_rank_na_option(self):
         tm.assert_almost_equal(ranks1.values, exp1)
 
         # top
-        ranks0 = self.frame.rank(na_option='top')
-        ranks1 = self.frame.rank(1, na_option='top')
+        ranks0 = float_frame.rank(na_option='top')
+        ranks1 = float_frame.rank(1, na_option='top')
 
-        fval0 = self.frame.fillna((self.frame.min() - 1).to_dict()).values
-        fval1 = self.frame.T
+        fval0 = float_frame.fillna((float_frame.min() - 1).to_dict()).values
+        fval1 = float_frame.T
         fval1 = fval1.fillna((fval1.min() - 1).to_dict()).T
         fval1 = fval1.fillna(np.inf).values
 
@@ -160,10 +160,10 @@ def test_rank_na_option(self):
         # descending
 
         # bottom
-        ranks0 = self.frame.rank(na_option='top', ascending=False)
-        ranks1 = self.frame.rank(1, na_option='top', ascending=False)
+        ranks0 = float_frame.rank(na_option='top', ascending=False)
+        ranks1 = float_frame.rank(1, na_option='top', ascending=False)
 
-        fvals = self.frame.fillna(np.inf).values
+        fvals = float_frame.fillna(np.inf).values
 
         exp0 = np.apply_along_axis(rankdata, 0, -fvals)
         exp1 = np.apply_along_axis(rankdata, 1, -fvals)
@@ -174,11 +174,11 @@ def test_rank_na_option(self):
         # descending
 
         # top
-        ranks0 = self.frame.rank(na_option='bottom', ascending=False)
-        ranks1 = self.frame.rank(1, na_option='bottom', ascending=False)
+        ranks0 = float_frame.rank(na_option='bottom', ascending=False)
+        ranks1 = float_frame.rank(1, na_option='bottom', ascending=False)
 
-        fval0 = self.frame.fillna((self.frame.min() - 1).to_dict()).values
-        fval1 = self.frame.T
+        fval0 = float_frame.fillna((float_frame.min() - 1).to_dict()).values
+        fval1 = float_frame.T
         fval1 = fval1.fillna((fval1.min() - 1).to_dict()).T
         fval1 = fval1.fillna(np.inf).values
 
@@ -192,11 +192,11 @@ def test_rank_na_option(self):
         msg = "na_option must be one of 'keep', 'top', or 'bottom'"
 
         with pytest.raises(ValueError, match=msg):
-            self.frame.rank(na_option='bad', ascending=False)
+            float_frame.rank(na_option='bad', ascending=False)
 
         # invalid type
         with pytest.raises(ValueError, match=msg):
-            self.frame.rank(na_option=True, ascending=False)
+            float_frame.rank(na_option=True, ascending=False)
 
     def test_rank_axis(self):
         # check if using axes' names gives the same result