diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst
index e8d9fd52cf352..213a7ab659dae 100644
--- a/doc/source/indexing.rst
+++ b/doc/source/indexing.rst
@@ -456,6 +456,39 @@ and :ref:`Advanced Indexing <indexing.advanced>` you may select along more than
 
    df2.loc[criterion & (df2['b'] == 'x'),'b':'c']
 
+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
+wherever the element is in the sequence of values.
+
+.. ipython:: python
+
+   df = DataFrame({'vals': [1, 2, 3, 4], 'ids': ['a', 'b', 'f', 'n'],
+                'ids2': ['a', 'n', 'c', 'n']})
+
+   values = ['a', 'b', 1, 3]
+
+   df.isin(values)
+
+Oftentimes you'll want to match certain values with certain columns.
+Just make values a ``dict`` where the key is the column, and the value is
+a list of items you want to check for.
+
+.. ipython:: python
+
+   values = {'ids': ['a', 'b'], 'vals': [1, 3]}
+
+   df.isin(values)
+
+You can also describe columns using integer location:
+
+.. ipython:: python
+
+   values = {0: ['a', 'b']}
+
+   df.isin(values, iloc=True)
+
+
 Where and Masking
 ~~~~~~~~~~~~~~~~~
 
diff --git a/doc/source/release.rst b/doc/source/release.rst
index 3b7d25789aa40..d03cdac14676a 100644
--- a/doc/source/release.rst
+++ b/doc/source/release.rst
@@ -54,6 +54,7 @@ pandas 0.12
   - Access to historical Google Finance data in pandas.io.data (:issue:`3814`)
   - DataFrame plotting methods can sample column colors from a Matplotlib
     colormap via the ``colormap`` keyword. (:issue:`3860`)
+  - Added ``isin`` method to DataFrame (:issue:`4211`)
 
 **Improvements to existing features**
 
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 401a7746953cb..22dc27ff977d9 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -5481,6 +5481,41 @@ def to_period(self, freq=None, axis=0, copy=True):
 
         return self._constructor(new_data)
 
+
+    def isin(self, values, iloc=False):
+        """
+        Return boolean DataFrame showing whether each element in the DataFrame is
+        contained in values.
+
+        Parameters
+        ----------
+        values : iterable or dictionary of columns to values
+        iloc : boolean, if passing a dict as values, describe columns using integer
+                        locations (default is to use labels)
+
+        Returns
+        -------
+
+        DataFrame of booleans
+        """
+        if isinstance(values, dict):
+            from collections import defaultdict
+            from pandas.tools.merge import concat
+            values = defaultdict(list, values)
+            if iloc:
+                return concat((self.iloc[:, [i]].isin(values[i])
+                                 for i, col in enumerate(self.columns)), axis=1)
+            else:
+                return concat((self.iloc[:, [i]].isin(values[col])
+                                 for i, col in enumerate(self.columns)), axis=1)
+
+
+        else:
+            return DataFrame(lib.ismember(self.values.ravel(),
+                                          set(values)).reshape(self.shape),
+                             self.index,
+                             self.columns)
+
     #----------------------------------------------------------------------
     # Deprecated stuff
 
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index a9df56a498f63..577cbfe9dc744 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -10633,6 +10633,55 @@ def _check_f(base, f):
         f = lambda x: x.rename({1: 'foo'}, inplace=True)
         _check_f(data.copy()['c'], f)
 
+    def test_isin(self):
+        # GH #4211
+        df = DataFrame({'vals': [1, 2, 3, 4], 'ids': ['a', 'b', 'f', 'n'],
+                        'ids2': ['a', 'n', 'c', 'n']},
+                        index=['foo', 'bar', 'baz', 'qux'])
+        other = ['a', 'b', 'c']
+
+        result = df.isin(other)
+        expected = DataFrame([df.loc[s].isin(other) for s in df.index])
+        assert_frame_equal(result, expected)
+
+    def test_isin_empty(self):
+        df = DataFrame({'A': ['a', 'b', 'c'], 'B': ['a', 'e', 'f']})
+        result = df.isin([])
+        expected = pd.DataFrame(False, df.index, df.columns)
+        assert_frame_equal(result, expected)
+
+    def test_isin_dict(self):
+        df = DataFrame({'A': ['a', 'b', 'c'], 'B': ['a', 'e', 'f']})
+        d = {'A': ['a']}
+
+        expected = DataFrame(False, df.index, df.columns)
+        expected.loc[0, 'A'] = True
+
+        result = df.isin(d)
+        assert_frame_equal(result, expected)
+
+        # non unique columns
+        df = DataFrame({'A': ['a', 'b', 'c'], 'B': ['a', 'e', 'f']})
+        df.columns = ['A', 'A']
+        expected = DataFrame(False, df.index, df.columns)
+        expected.loc[0, 'A'] = True
+        result = df.isin(d)
+        assert_frame_equal(result, expected)
+
+        # iloc
+        df = DataFrame({'A': ['a', 'b', 'c'], 'B': ['a', 'e', 'f']})
+        d = {0: ['a']}
+        expected = DataFrame(False, df.index, df.columns)
+
+        # without using iloc
+        result = df.isin(d)
+        assert_frame_equal(result, expected)        
+
+        # using iloc
+        result = df.isin(d, iloc=True)
+        expected.iloc[0, 0] = True
+        assert_frame_equal(result, expected)        
+
 
 if __name__ == '__main__':
     # unittest.main()