diff --git a/pandas/core/indexes/datetimelike.py b/pandas/core/indexes/datetimelike.py
index e673bfe411cb4..1c41488973978 100644
--- a/pandas/core/indexes/datetimelike.py
+++ b/pandas/core/indexes/datetimelike.py
@@ -82,21 +82,84 @@ class TimelikeOps(object):
 
     _round_doc = (
         """
-        %s the index to the specified freq
+        {op} the data to the specified `freq`.
 
         Parameters
         ----------
-        freq : freq string/object
+        freq : str or Offset
+            The frequency level to {op} the index to. Must be a fixed
+            frequency like 'S' (second) not 'ME' (month end). See
+            :ref:`frequency aliases <timeseries.offset_aliases>` for
+            a list of possible `freq` values.
 
         Returns
         -------
-        index of same type
+        DatetimeIndex, TimedeltaIndex, or Series
+            Index of the same type for a DatetimeIndex or TimedeltaIndex,
+            or a Series with the same index for a Series.
 
         Raises
         ------
-        ValueError if the freq cannot be converted
+        ValueError if the `freq` cannot be converted.
+
+        Examples
+        --------
+        **DatetimeIndex**
+
+        >>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min')
+        >>> rng
+        DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00',
+                       '2018-01-01 12:01:00'],
+                      dtype='datetime64[ns]', freq='T')
+        """)
+
+    _round_example = (
+        """>>> rng.round('H')
+        DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',
+                       '2018-01-01 12:00:00'],
+                      dtype='datetime64[ns]', freq=None)
+
+        **Series**
+
+        >>> pd.Series(rng).dt.round("H")
+        0   2018-01-01 12:00:00
+        1   2018-01-01 12:00:00
+        2   2018-01-01 12:00:00
+        dtype: datetime64[ns]
         """)
 
+    _floor_example = (
+        """>>> rng.floor('H')
+        DatetimeIndex(['2018-01-01 11:00:00', '2018-01-01 12:00:00',
+                       '2018-01-01 12:00:00'],
+                      dtype='datetime64[ns]', freq=None)
+
+        **Series**
+
+        >>> pd.Series(rng).dt.floor("H")
+        0   2018-01-01 11:00:00
+        1   2018-01-01 12:00:00
+        2   2018-01-01 12:00:00
+        dtype: datetime64[ns]
+        """
+    )
+
+    _ceil_example = (
+        """>>> rng.ceil('H')
+        DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',
+                       '2018-01-01 13:00:00'],
+                      dtype='datetime64[ns]', freq=None)
+
+        **Series**
+
+        >>> pd.Series(rng).dt.ceil("H")
+        0   2018-01-01 12:00:00
+        1   2018-01-01 12:00:00
+        2   2018-01-01 13:00:00
+        dtype: datetime64[ns]
+        """
+    )
+
     def _round(self, freq, rounder):
         # round the local times
         values = _ensure_datetimelike_to_i8(self)
@@ -111,15 +174,15 @@ def _round(self, freq, rounder):
         return self._ensure_localized(
             self._shallow_copy(result, **attribs))
 
-    @Appender(_round_doc % "round")
+    @Appender((_round_doc + _round_example).format(op="round"))
     def round(self, freq, *args, **kwargs):
         return self._round(freq, np.round)
 
-    @Appender(_round_doc % "floor")
+    @Appender((_round_doc + _floor_example).format(op="floor"))
     def floor(self, freq):
         return self._round(freq, np.floor)
 
-    @Appender(_round_doc % "ceil")
+    @Appender((_round_doc + _ceil_example).format(op="ceil"))
     def ceil(self, freq):
         return self._round(freq, np.ceil)