diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst
index bc9dcdccfc2e1..dc8c42fd11989 100644
--- a/doc/source/indexing.rst
+++ b/doc/source/indexing.rst
@@ -519,21 +519,14 @@ of the DataFrame):
 
    df[df['A'] > 0]
 
-Consider the ``isin`` method of Series, which returns a boolean vector that is
-true wherever the Series elements exist in the passed list. This allows you to
-select rows where one or more columns have values you want:
+List comprehensions and ``map`` method of Series can also be used to produce
+more complex criteria:
 
 .. ipython:: python
 
    df2 = DataFrame({'a' : ['one', 'one', 'two', 'three', 'two', 'one', 'six'],
                     'b' : ['x', 'y', 'y', 'x', 'y', 'x', 'x'],
                     'c' : randn(7)})
-   df2[df2['a'].isin(['one', 'two'])]
-
-List comprehensions and ``map`` method of Series can also be used to produce
-more complex criteria:
-
-.. ipython:: python
 
    # only want 'two' or 'three'
    criterion = df2['a'].map(lambda x: x.startswith('t'))
@@ -553,6 +546,26 @@ and :ref:`Advanced Indexing <indexing.advanced>` you may select along more than
 
    df2.loc[criterion & (df2['b'] == 'x'),'b':'c']
 
+.. _indexing.basics.indexing_isin:
+
+Indexing with isin
+~~~~~~~~~~~~~~~~~~
+
+Consider the ``isin`` method of Series, which returns a boolean vector that is
+true wherever the Series elements exist in the passed list. This allows you to
+select rows where one or more columns have values you want:
+
+.. ipython:: python
+
+   s = Series(np.arange(5),index=np.arange(5)[::-1],dtype='int64')
+
+   s
+
+   s.isin([2, 4])
+
+   s[s.isin([2, 4])]
+
+
 DataFrame also has an ``isin`` method.  When calling ``isin``, pass a set of
 values as either an array or dict.  If values is an array, ``isin`` returns
 a DataFrame of booleans that is the same shape as the original DataFrame, with True
@@ -585,6 +598,17 @@ You can also describe columns using integer location:
 
    df.isin(values, iloc=True)
 
+Combine DataFrame's ``isin`` with the ``any()`` and ``all()`` methods to
+quickly select subsets of your data that meet a given criteria.
+To select a row where each column meets its own criterion:
+
+.. ipython:: python
+
+  values = {'ids': ['a', 'b'], 'ids2': ['a', 'c'], 'vals': [1, 3]}
+
+  row_mask = df.isin(values).all(1)
+
+  df[row_mask]
 
 The :meth:`~pandas.DataFrame.where` Method and Masking
 ------------------------------------------------------
diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt
index 1f9dc8d7dad81..090be81a3ee7c 100644
--- a/doc/source/v0.13.0.txt
+++ b/doc/source/v0.13.0.txt
@@ -516,6 +516,22 @@ Experimental
     For more details see the :ref:`indexing documentation on query
     <indexing.query>`.
 
+  - DataFrame now has an ``isin`` method that can be used to easily check whether the DataFrame's values are contained in an iterable.  Use a dictionary if you'd like to check specific iterables for specific columns or rows.
+
+    .. ipython:: python
+
+      df = pd.DataFrame({'A': [1, 2, 3], 'B': ['d', 'e', 'f']})
+      df.isin({'A': [1, 2], 'B': ['e', 'f']})
+
+  The ``isin`` method plays nicely with boolean indexing. To get the rows where each condition is met:
+
+    .. ipython:: python
+
+      mask = df.isin({'A': [1, 2], 'B': ['e', 'f']})
+      df[mask.all(1)]
+
+  See the :ref:`documentation<indexing.basics.indexing_isin>` for more.
+
 .. _whatsnew_0130.refactoring:
 
 Internal Refactoring