diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst
index 36ffe8806f373..fa8d036b655e6 100644
--- a/doc/source/timeseries.rst
+++ b/doc/source/timeseries.rst
@@ -657,18 +657,25 @@ With no defaults.
 Truncating & Fancy Indexing
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-A ``truncate`` convenience function is provided that is equivalent to slicing:
+A ``truncate`` convenience function is provided that is similar to slicing.
+Note that ``truncate`` assumes a 0 value for any unspecified date component
+in a ``DatetimeIndex`` in contrast to slicing which returns any partially
+matching dates:
 
 .. ipython:: python
 
-   ts.truncate(before='10/31/2011', after='12/31/2011')
+   rng2 = pd.date_range('2011-01-01', '2012-01-01', freq='W')
+   ts2 = pd.Series(np.random.randn(len(rng2)), index=rng2)
+
+   ts2.truncate(before='2011-11', after='2011-12')
+   ts2['2011-11':'2011-12']
 
 Even complicated fancy indexing that breaks the ``DatetimeIndex`` frequency
 regularity will result in a ``DatetimeIndex``, although frequency is lost:
 
 .. ipython:: python
 
-   ts[[0, 2, 6]].index
+   ts2[[0, 2, 6]].index
 
 .. _timeseries.components:
 
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 35a26702ad15a..52bebaed4a368 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -6324,23 +6324,72 @@ def tshift(self, periods=1, freq=None, axis=0):
         return self._constructor(new_data).__finalize__(self)
 
     def truncate(self, before=None, after=None, axis=None, copy=True):
-        """Truncates a sorted NDFrame before and/or after some particular
-        index value. If the axis contains only datetime values, before/after
-        parameters are converted to datetime values.
+        """
+        Truncates a sorted DataFrame/Series before and/or after some
+        particular index value. If the axis contains only datetime values,
+        before/after parameters are converted to datetime values.
 
         Parameters
         ----------
-        before : date
-            Truncate before index value
-        after : date
-            Truncate after index value
-        axis : the truncation axis, defaults to the stat axis
+        before : date, string, int
+            Truncate all rows before this index value
+        after : date, string, int
+            Truncate all rows after this index value
+        axis : {0 or 'index', 1 or 'columns'}
+
+            * 0 or 'index': apply truncation to rows
+            * 1 or 'columns': apply truncation to columns
+            Default is stat axis for given data type (0 for Series and
+            DataFrames, 1 for Panels)
         copy : boolean, default is True,
             return a copy of the truncated section
 
         Returns
         -------
         truncated : type of caller
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'],
+        ...                    'B': ['f', 'g', 'h', 'i', 'j'],
+        ...                    'C': ['k', 'l', 'm', 'n', 'o']},
+        ...                    index=[1, 2, 3, 4, 5])
+        >>> df.truncate(before=2, after=4)
+           A  B  C
+        2  b  g  l
+        3  c  h  m
+        4  d  i  n
+        >>> df = pd.DataFrame({'A': [1, 2, 3, 4, 5],
+        ...                    'B': [6, 7, 8, 9, 10],
+        ...                    'C': [11, 12, 13, 14, 15]},
+        ...                    index=['a', 'b', 'c', 'd', 'e'])
+        >>> df.truncate(before='b', after='d')
+           A  B   C
+        b  2  7  12
+        c  3  8  13
+        d  4  9  14
+
+        The index values in ``truncate`` can be datetimes or string
+        dates. Note that ``truncate`` assumes a 0 value for any unspecified
+        date component in a ``DatetimeIndex`` in contrast to slicing which
+        returns any partially matching dates.
+
+        >>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s')
+        >>> df = pd.DataFrame(index=dates, data={'A': 1})
+        >>> df.truncate('2016-01-05', '2016-01-10').tail()
+                             A
+        2016-01-09 23:59:56  1
+        2016-01-09 23:59:57  1
+        2016-01-09 23:59:58  1
+        2016-01-09 23:59:59  1
+        2016-01-10 00:00:00  1
+        >>> df.loc['2016-01-05':'2016-01-10', :].tail()
+                             A
+        2016-01-10 23:59:55  1
+        2016-01-10 23:59:56  1
+        2016-01-10 23:59:57  1
+        2016-01-10 23:59:58  1
+        2016-01-10 23:59:59  1
         """
 
         if axis is None: