diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py
index efae7c63a9d0e..4012629aa3c90 100644
--- a/pandas/tools/merge.py
+++ b/pandas/tools/merge.py
@@ -1398,9 +1398,11 @@ def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
            copy=True):
     """
     Concatenate pandas objects along a particular axis with optional set logic
-    along the other axes. Can also add a layer of hierarchical indexing on the
-    concatenation axis, which may be useful if the labels are the same (or
-    overlapping) on the passed axis number
+    along the other axes.
+
+    Can also add a layer of hierarchical indexing on the concatenation axis,
+    which may be useful if the labels are the same (or overlapping) on
+    the passed axis number.
 
     Parameters
     ----------
@@ -1436,13 +1438,141 @@ def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
     copy : boolean, default True
         If False, do not copy data unnecessarily
 
-    Notes
-    -----
-    The keys, levels, and names arguments are all optional
-
     Returns
     -------
     concatenated : type of objects
+
+    Notes
+    -----
+    The keys, levels, and names arguments are all optional.
+
+    A walkthrough of how this method fits in with other tools for combining
+    panda objects can be found `here
+    <http://pandas.pydata.org/pandas-docs/stable/merging.html>`__.
+
+    See Also
+    --------
+    Series.append
+    DataFrame.append
+    DataFrame.join
+    DataFrame.merge
+
+    Examples
+    --------
+    Combine two ``Series``.
+
+    >>> s1 = pd.Series(['a', 'b'])
+    >>> s2 = pd.Series(['c', 'd'])
+    >>> pd.concat([s1, s2])
+    0    a
+    1    b
+    0    c
+    1    d
+    dtype: object
+
+    Clear the existing index and reset it in the result
+    by setting the ``ignore_index`` option to ``True``.
+
+    >>> pd.concat([s1, s2], ignore_index=True)
+    0    a
+    1    b
+    2    c
+    3    d
+    dtype: object
+
+    Add a hierarchical index at the outermost level of
+    the data with the ``keys`` option.
+
+    >>> pd.concat([s1, s2], keys=['s1', 's2',])
+    s1  0    a
+        1    b
+    s2  0    c
+        1    d
+    dtype: object
+
+    Label the index keys you create with the ``names`` option.
+
+    >>> pd.concat([s1, s2], keys=['s1', 's2'],
+    ...           names=['Series name', 'Row ID'])
+    Series name  Row ID
+    s1           0         a
+                 1         b
+    s2           0         c
+                 1         d
+    dtype: object
+
+    Combine two ``DataFrame`` objects with identical columns.
+
+    >>> df1 = pd.DataFrame([['a', 1], ['b', 2]],
+    ...                    columns=['letter', 'number'])
+    >>> df1
+      letter  number
+    0      a       1
+    1      b       2
+    >>> df2 = pd.DataFrame([['c', 3], ['d', 4]],
+    ...                    columns=['letter', 'number'])
+    >>> df2
+      letter  number
+    0      c       3
+    1      d       4
+    >>> pd.concat([df1, df2])
+      letter  number
+    0      a       1
+    1      b       2
+    0      c       3
+    1      d       4
+
+    Combine ``DataFrame`` objects with overlapping columns
+    and return everything. Columns outside the intersection will
+    be filled with ``NaN`` values.
+
+    >>> df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],
+    ...                    columns=['letter', 'number', 'animal'])
+    >>> df3
+      letter  number animal
+    0      c       3    cat
+    1      d       4    dog
+    >>> pd.concat([df1, df3])
+      animal letter  number
+    0    NaN      a       1
+    1    NaN      b       2
+    0    cat      c       3
+    1    dog      d       4
+
+    Combine ``DataFrame`` objects with overlapping columns
+    and return only those that are shared by passing ``inner`` to
+    the ``join`` keyword argument.
+
+    >>> pd.concat([df1, df3], join="inner")
+      letter  number
+    0      a       1
+    1      b       2
+    0      c       3
+    1      d       4
+
+    Combine ``DataFrame`` objects horizontally along the x axis by
+    passing in ``axis=1``.
+
+    >>> df4 = pd.DataFrame([['bird', 'polly'], ['monkey', 'george']],
+    ...                    columns=['animal', 'name'])
+    >>> pd.concat([df1, df4], axis=1)
+      letter  number  animal    name
+    0      a       1    bird   polly
+    1      b       2  monkey  george
+
+    Prevent the result from including duplicate index values with the
+    ``verify_integrity`` option.
+
+    >>> df5 = pd.DataFrame([1], index=['a'])
+    >>> df5
+       0
+    a  1
+    >>> df6 = pd.DataFrame([2], index=['a'])
+    >>> df6
+       0
+    a  2
+    >>> pd.concat([df5, df6], verify_integrity=True)
+    ValueError: Indexes have overlapping values: ['a']
     """
     op = _Concatenator(objs, axis=axis, join_axes=join_axes,
                        ignore_index=ignore_index, join=join,