diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index d4ce8dc166b09..22677b19192e1 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -7089,6 +7089,9 @@ def quantile(self, q=0.5, axis=0, numeric_only=True,
             0 <= q <= 1, the quantile(s) to compute
         axis : {0, 1, 'index', 'columns'} (default 0)
             0 or 'index' for row-wise, 1 or 'columns' for column-wise
+        numeric_only : boolean, default True
+            If False, the quantile of datetime and timedelta data will be
+            computed as well
         interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
             .. versionadded:: 0.18.0
 
@@ -7116,7 +7119,7 @@ def quantile(self, q=0.5, axis=0, numeric_only=True,
         --------
 
         >>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
-                           columns=['a', 'b'])
+                              columns=['a', 'b'])
         >>> df.quantile(.1)
         a    1.3
         b    3.7
@@ -7126,6 +7129,20 @@ def quantile(self, q=0.5, axis=0, numeric_only=True,
         0.1  1.3   3.7
         0.5  2.5  55.0
 
+        Specifying `numeric_only=False` will also compute the quantile of
+        datetime and timedelta data.
+
+        >>> df = pd.DataFrame({'A': [1, 2],
+                               'B': [pd.Timestamp('2010'),
+                                     pd.Timestamp('2011')],
+                               'C': [pd.Timedelta('1 days'),
+                                     pd.Timedelta('2 days')]})
+        >>> df.quantile(0.5, numeric_only=False)
+        A                    1.5
+        B    2010-07-02 12:00:00
+        C        1 days 12:00:00
+        Name: 0.5, dtype: object
+
         See Also
         --------
         pandas.core.window.Rolling.quantile